| 1. |
- Jern, Mikael, 1946-, et al.
(författare)
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The GAV Toolkit for Multiple Linked Views
- 2007
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Ingår i: Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization, 2007. CMV '07.. - Los Alamitos, CA, USA : IEEE Computer Society. - 0769529038 ; , s. 85-97
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Konferensbidrag (refereegranskat)abstract
- Implementing InfoVis multivariate data tools, timelinked coordinated views and visual dynamic queries with conditioning from scratch is not a simple programming task. Our research objective is to develop a generic GeoAnalytics visualization (GAV) component toolkit, based on the principles behind visual analytics (VA), for dynamically exploring time-varying, geographically referenced and multivariate attributes simultaneously. GAV includes components based on a synergy of technologies from information visualization, geovisualization and scientific visualization. Our research concentrates on improving visual user interfaces (VUI) methods and trying to extend existing visual representation techniques. The effectiveness of our proposed component toolkit and framework is demonstrated in two customized applications GeoWizard analysing multivariate energy usage data for Swedish municipalities and MD-Explorer exploring multivariate data using novel interactive ternary diagrams. We use parallel coordinates with embedded visual inquiry methods that serves as a visual control panel for dynamically linked and coordinated views. Finally, discoveries made during the visual exploration process can be captured and organized in a format for later recall and communication to others.
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| 2. |
- Johansson Fernstad, Sara, 1980-
(författare)
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Algorithmically Guided Information Visualization : Explorative Approaches for High Dimensional, Mixed and Categorical Data
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. Information visualization provides methods for visual analysis of complex data but, as the amounts of gathered data increase, the challenges of visual analysis become more complex.This thesis presents work utilizing algorithmically extracted patterns as guidance during interactive data exploration processes, employing information visualization techniques. It provides efficient analysis by taking advantage of fast pattern identification techniques as well as making use of the domain expertise of the analyst. In particular, the presented research is concerned with the issues of analysing categorical data, where the values are names without any inherent order or distance; mixed data, including a combination of categorical and numerical data; and high dimensional data, including hundreds or even thousands of variables.The contributions of the thesis include a quantification method, assigning numerical values to categorical data, which utilizes an automated method to define category similarities based on underlying data structures, and integrates relationships within numerical variables into the quantification when dealing with mixed data sets. The quantification is incorporated in an interactive analysis pipeline where it provides suggestions for numerical representations, which may interactively be adjusted by the analyst. The interactive quantification enables exploration using commonly available visualization methods for numerical data. Within the context of categorical data analysis, this thesis also contributes the first user study evaluating the performance of what are currently the two main visualization approaches for categorical data analysis.Furthermore, this thesis contributes two dimensionality reduction approaches, which aim at preserving structure while reducing dimensionality, and provide flexible and user-controlled dimensionality reduction. Through algorithmic quality metric analysis, where each metric represents a structure of interest, potentially interesting variables are extracted from the high dimensional data. The automatically identified structures are visually displayed, using various visualization methods, and act as guidance in the selection of interesting variable subsets for further analysis. The visual representations furthermore provide overview of structures within the high dimensional data set and may, through this, aid in focusing subsequent analysis, as well as enabling interactive exploration of the full high dimensional data set and selected variable subsets. The thesis also contributes the application of algorithmically guided approaches for high dimensional data exploration in the rapidly growing field of microbiology, through the design and development of a quality-guided interactive system in collaboration with microbiologists.
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| 3. |
- Johansson, Sara, 1980-, et al.
(författare)
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Interactive Exploration of Ingredient Mixtures Using Multiple Coordinated Views
- 2009
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Ingår i: Proceedings Information Visualization, IV 2009. - Los Alamitos, CA, USA : IEEE Computer Society. - 9780769537337 ; , s. 210-218
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Konferensbidrag (refereegranskat)abstract
- The complex nature of multivariate data sets calls forhigh interactive performance and intuitive metaphors. Aspecific type of multivariate data is where the variables sum up to a constant, here defined as multicomponent data.This application paper presents an interactive applicationfor analysis of modelled multicomponent data. The aim isto find high performance variable combinations that fulfil some requested properties. The application is basedon coordinated views that include parallel coordinates, a ternary diagram, a 2D scatter plot and a line plot. It supports numerous interaction techniques enabling fast analysisof complex patterns in multicomponent data sets. The application is developed in collaboration with researchers within the fields of statistics and chemistry. An informal usability evaluation indicates that the interactive nature ofthe application clearly facilitates the analysis process.
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| 4. |
- Bjerke, Lina, 1980-, et al.
(författare)
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Framtidens kompetensförsörjning i Skaraborg
- 2019
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Syftet med denna studie är att kartlägga kompetensutbudet i Skaraborg och visa hur utbud av olika typer av arbetskraft har utvecklats på kort och lång sikt. I studien jämförs utvecklingen i Skaraborg med utvecklingen i arbetsmarknader i övriga Sverige, i syfte att klargöra om utvecklingen i Skaraborg särskiljer sig på något sätt. Studien syftar också till att identifiera om det finns yrken eller branscher där det inomregionala kompetensutbudet är otillräckligt för att svara mot regionens behov. Detta görs dels genom att undersöka mönster för arbetspendling, både inom regionen och från andra regioner, dels genom att undersöka hur väl arbetstagares utbildning matchar det yrke som arbetstagaren utför. Målet är att dessa analyser sammantaget ska ge en nyanserad bild av kompetensutbudet i Skaraborg och peka på områden där kompetenstillgången i regionen riskerar att hämma näringslivets utveckling. På så vis ämnar denna studie att komplettera tidigare studier kring kompetensbehov för att ge en mer sammansatt bild av både utbud och efterfrågan på kompetens i Skaraborg.
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| 5. |
- Bjerke, Lina, 1980-, et al.
(författare)
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Innovation in agriculture : An analysis of Swedish agricultural and non-agricultural firms
- 2022
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Ingår i: Food Policy. - : Elsevier. - 0306-9192 .- 1873-5657. ; 109
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Tidskriftsartikel (refereegranskat)abstract
- This paper contributes to the literature on innovation in agri-food sectors. It presents a quantitative analysis of how innovation and innovation capacities differ between agricultural firms and firms in other sectors. With unique survey data on Swedish firms, we show that one-third of the firms in the agriculture sector are innovation creators. Moreover, incremental innovations in the form of firm-level technology adoption are not more prevalent in agriculture than in other sectors. These results indicate that agriculture does not appear as a special case concerning innovation output, besides their higher incidence of process innovations. The peculiarity of the sector rather seems to be related to firm-level innovation capacities. Our results suggest that agriculture is different in how they source relevant knowledge, which is a finding that could question the design of innovation support policies that emphasize collaborative research. Our findings rather point to a need of strengthening in-house knowledge capacities in agricultural firms. That would improve their capacity to participate in and benefit from collaborations with partners operating at higher levels in the knowledge system.
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| 6. |
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| 7. |
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| 8. |
- Borgmästars, Emmy, et al.
(författare)
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Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank
- 2024
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Ingår i: Journal of Gastrointestinal Oncology. - : AME Publishing Company. - 2078-6891 .- 2219-679X. ; 15:2, s. 755-767
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Tidskriftsartikel (refereegranskat)abstract
- Background: Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.Methods: We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score.Results: Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714–0.854] compared to 0.681 (95% CI: 0.597–0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1].Conclusions: Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.
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| 9. |
- Borgmästars, Emmy, et al.
(författare)
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Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank
- 2024
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Ingår i: Journal of Gastrointestinal Oncology. - : AME Publishing Company. - 2078-6891 .- 2219-679X. ; 15:2, s. 755-767
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Tidskriftsartikel (refereegranskat)abstract
- Background: Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.Methods: We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score.Results: Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714–0.854] compared to 0.681 (95% CI: 0.597–0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1].Conclusions: Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.
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| 10. |
- Ewert, Sara, 1974, et al.
(författare)
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Dynamic expression of the angiotensin II type 2 receptor and duodenal mucosal alkaline secretion in the Sprague-Dawley rat
- 2006
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Ingår i: Experimental Physiology. - : Wiley. - 0958-0670. ; 91:1, s. 191-199
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Tidskriftsartikel (refereegranskat)abstract
- Activation of angiotensin II type 2 receptors (AT2R) has been shown to stimulate duodenal mucosal alkaline secretion (DMAS) in Sprague-Dawley rats (S-D). This finding could not be confirmed in another line of S-D, and the present study investigates whether the level of AT2R expression determines the response to the AT2R agonist CGP42112A. DMAS was measured in anaesthetized rats using in situ pH-stat titration. Real-time PCR and Western blot were used to assess AT1R and AT2R RNA and protein expression, respectively. CGP42112A (0.1 microg kg(-1)min(-1) I.V.) elicited a 45% net increase in DMAS in the previous S-D line studied, whereas no change occurred in the new S-D line. Luminal administration of prostaglandin E2 (10(-5) M) increased DMAS similarly in both S-D lines. AT2R protein expression was significantly higher in tissue from the previous line compared to the new line. Individual AT1R to AT2R ratios (RNA and protein) were significantly higher in the new line compared to the previous S-D line. In the new S-D line intravenous infusion of angiotensin II (Ang II; 10 microg kg(-1) h(-1)) over 120 min significantly lowered the duodenal AT1aR to AT2R RNA ratio. Prolonged Ang II infusion over 240 min increased AT2R protein expression and evoked a 42% stimulatory response in DMAS to CGP42112A. The level of local AT2R expression determines the effect of the AT2R agonist CGP42112A on rat duodenal mucosal alkaline secretion. AT2R expression should be confirmed before interpreting the experimental effects of pharmacological interferences with this receptor.
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