| 1. |
- Abate, E., et al.
(författare)
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Combined performance tests before installation of the ATLAS Semiconductor and Transition Radiation Tracking Detectors
- 2008
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Ingår i: Journal of Instrumentation. - 1748-0221. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- The ATLAS (A Toroidal LHC ApparatuS) Inner Detector provides charged particle tracking in the centre of the ATLAS experiment at the Large Hadron Collider (LHC). The Inner Detector consists of three subdetectors: the Pixel Detector, the Semiconductor Tracker (SCT), and the Transition Radiation Tracker (TRT). This paper summarizes the tests that were carried out at the final stage of SCT+TRT integration prior to their installation in ATLAS. The combined operation and performance of the SCT and TRT barrel and endcap detectors was investigated through a series of noise tests, and by recording the tracks of cosmic rays. This was a crucial test of hardware and software of the combined tracker detector systems. The results of noise and cross-talk tests on the SCT and TRT in their final assembled configuration, using final readout and supply hardware and software, are reported. The reconstruction and analysis of the recorded cosmic tracks allowed testing of the offline analysis chain and verification of basic tracker performance parameters, such as efficiency and spatial resolution, in combined operation before installation.
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| 2. |
- Adomas, Aleksandra, et al.
(författare)
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Identification and analysis of differentially expressed cDNAs during nonself-competitive interaction between Phlebiopsis gigantea and Heterobasidion parviporum
- 2006
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Ingår i: FEMS Microbiology Ecology. - : Blackwell Publishing. - 0168-6496 .- 1574-6941. ; 57:1, s. 26-39
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Tidskriftsartikel (refereegranskat)abstract
- The molecular factors regulating interspecific interaction between the saprotrophic biocontrol fungus Phlebiopsis gigantea and the conifer pathogen Heterobasidion parviporum were investigated. We constructed cDNA libraries and used expressed sequence tag analysis for the identification and characterization of genes expressed during the self and nonself-hyphal interaction. cDNA clones from either the pathogen or biocontrol agent were arrayed on nylon membrane filters and differentially screened with cDNA probes made from mycelia forming the barrage zone during nonself-interactions, mycelia growing outside the barrage zones or monocultures. BlastX analysis of the differentially expressed clones led to the identification of genes with diverse functions, including those with potential as virulence factors, such as hydrophobins. Because of the high sequence conservation (r2 = 0.81) between P. gigantea and H. parviporum, a selected number of genes from either fungus were used to monitor the expression profile under varying interaction conditions by virtual northern blot. The results are discussed with respect to the potential role of the induced genes during the nonself-competitive interaction for space and nutrients between P. gigantea and H. parviporum.
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| 3. |
- Berntsson, Lars-Olof, et al.
(författare)
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EAST-ADL 2.0 Specification
- 2008
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Rapport (refereegranskat)abstract
- This specification of the EAST ADL 2.0 is based on the EAST-ADL developed in the EAST EEA projectand has been further refined and harmonized with on-going modelling appraches in the automotiveindustry. It presents the modeling infrastructure, i.e. how the modeling elements should be represented inthe language and the UML representation. For each package a usage example is provided.The EAST-ADL 2.0 is harmonized with AUTOSAR.The metamodel and UML profile of EAST ADL 2.0 is defined in two steps: A domain (automotive)metamodel is defined, capturing only the domain specific needs of the language, without adding the UML2details. The basic concepts of UML are used for this purpose, such as classes, compositions andassociations. Based on the domain metamodel, a UML2 profile for the domain metamodel is defined,specifying stereotypes with properties and constraints.Comments on the content of this document are welcomed, and should be directed to .Please download the latest available specification and the XMI file ready for use in UML2 tools from the website.
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| 4. |
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| 5. |
- Eklund, Fredrik, et al.
(författare)
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Variation in fracture rates by country may not be explained by differences in bone mass
- 2009
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Ingår i: Calcified Tissue International. - : Springer Science and Business Media LLC. - 0171-967X .- 1432-0827. ; 85:1, s. 10-16
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Tidskriftsartikel (refereegranskat)abstract
- It is unclear whether the high fracture incidence in Sweden compared with other countries is related to low bone mass. We present and compare bone mineral density (BMD, g/cm(2)) at the femoral neck in a mainly osteoporotic referral population consisting of 2,031 men and 6,932 women with that of previous population-based cohorts. BMD measurements were collected at a single study center in Sweden, and data on validated hip fractures were collected from the corresponding health-care district and the cohort investigated. The BMD values of our cohort were similar to those of population-based cohorts from other countries. In contrast, the total incidence of hip fractures in 80-year-old women and men in the health-care district where our BMD measurements were performed was high (1.8% and 0.9%, respectively). The correlation between age and BMD was more negative in men aged 20-49 years than in women of the same age group (-0.011 vs. -0.006 g/cm(2) yearly, P < 0.001). In contrast, at 50-80 years of age, more negative regression coefficients were seen in women (-0.007 vs. -0.004 g/cm(2) yearly, P < 0.001 for comparison). In conclusion, a low BMD may not be the key factor explaining Sweden's comparatively high fracture incidence. In our cross-sectional data, age trends in BMD at the femoral neck differ between men and women. It would be highly interesting to further study the underlying causes of the global variations in fracture incidence rates.
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| 6. |
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| 7. |
- Eklund, Martin, 1978-
(författare)
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eScience Approaches to Model Selection and Assessment : Applications in Bioinformatics
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- High-throughput experimental methods, such as DNA and protein microarrays, have become ubiquitous and indispensable tools in biology and biomedicine, and the number of high-throughput technologies is constantly increasing. They provide the power to measure thousands of properties of a biological system in a single experiment and have the potential to revolutionize our understanding of biology and medicine. However, the high expectations on high-throughput methods are challenged by the problem to statistically model the wealth of data in order to translate it into concrete biological knowledge, new drugs, and clinical practices. In particular, the huge number of properties measured in high-throughput experiments makes statistical model selection and assessment exigent. To use high-throughput data in critical applications, it must be warranted that the models we construct reflect the underlying biology and are not just hypotheses suggested by the data. We must furthermore have a clear picture of the risk of making incorrect decisions based on the models. The rapid improvements of computers and information technology have opened up new ways of how the problem of model selection and assessment can be approached. Specifically, eScience, i.e. computationally intensive science that is carried out in distributed network envi- ronments, provides computational power and means to efficiently access previously acquired scientific knowledge. This thesis investigates how we can use eScience to improve our chances of constructing biologically relevant models from high-throughput data. Novel methods for model selection and assessment that leverage on computational power and on prior scientific information to "guide" the model selection to models that a priori are likely to be relevant are proposed. In addition, a software system for deploying new methods and make them easily accessible to end users is presented.
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| 8. |
- Eklund, Martin, et al.
(författare)
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SimSel - a new simulation feature selection method I
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In pharmaceutical research there are datasets describing the interactions between proteins and molecules. The datasets include a huge number of independent variables (features) and the response variable is typically the binding strength. Thus, one of the most challenging problems is to find the features that have a real influence on the binding strength.Here we present a feature selection method. The principle of the algorithm is to disturb each single feature by adding pseudo errors and to study the influence on the quality of the model fit.The main idea is that the change of unimportant features has no effect on the binding strength.
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| 9. |
- Eklund, Martin, et al.
(författare)
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The C1C2 : a framework for simultaneous model selection and assessment
- 2008
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 9, s. 360-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: There has been recent concern regarding the inability of predictive modeling approaches to generalize to new data. Some of the problems can be attributed to improper methods for model selection and assessment. Here, we have addressed this issue by introducing a novel and general framework, the C1C2, for simultaneous model selection and assessment. The framework relies on a partitioning of the data in order to separate model choice from model assessment in terms of used data. Since the number of conceivable models in general is vast, it was also of interest to investigate the employment of two automatic search methods, a genetic algorithm and a brute-force method, for model choice. As a demonstration, the C1C2 was applied to simulated and real-world datasets. A penalized linear model was assumed to reasonably approximate the true relation between the dependent and independent variables, thus reducing the model choice problem to a matter of variable selection and choice of penalizing parameter. We also studied the impact of assuming prior knowledge about the number of relevant variables on model choice and generalization error estimates. The results obtained with the C1C2 were compared to those obtained by employing repeated K-fold cross-validation for choosing and assessing a model. RESULTS: The C1C2 framework performed well at finding the true model in terms of choosing the correct variable subset and producing reasonable choices for the penalizing parameter, even in situations when the independent variables were highly correlated and when the number of observations was less than the number of variables. The C1C2 framework was also found to give accurate estimates of the generalization error. Prior information about the number of important independent variables improved the variable subset choice but reduced the accuracy of generalization error estimates. Using the genetic algorithm worsened the model choice but not the generalization error estimates, compared to using the brute-force method. The results obtained with repeated K-fold cross-validation were similar to those produced by the C1C2 in terms of model choice, however a lower accuracy of the generalization error estimates was observed. CONCLUSION: The C1C2 framework was demonstrated to work well for finding the true model within a penalized linear model class and accurately assess its generalization error, even for datasets with many highly correlated independent variables, a low observation-to-variable ratio, and model assumption deviations. A complete separation of the model choice and the model assessment in terms of data used for each task improves the estimates of the generalization error.
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| 10. |
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