| 1. |
- Holgersson, G., et al.
(författare)
-
Molecular profiling using tissue microarrays as a tool to identify predictive biomarkers in laryngeal cancer treated with radiotherapy
- 2010
-
Ingår i: Cancer Genomics & Proteomics. - 1109-6535 .- 1790-6245. ; 7:1, s. 1-7
-
Tidskriftsartikel (refereegranskat)abstract
- Aim: To explore the usefulness of the expression of five potential cancer biomarkers in predicting outcome in patients with laryngeal cancer. Materials and Methods: In the present study, the Swedish National Cancer Registry databases were used to identify patients with laryngeal cancer diagnosed during the years 1978-2004 in the Uppsala-Örebro region and treated with radiotherapy. The expression of Ki-67, MutS homolog 2, (MSH2), p53, B-cell CLL/lymphoma 2 (Bcl-2) and cyclin D1 in the cancer cells was assessed immunohistochemically using tissue microarrays (TMAs) and its predictve value on survival and relapse was analyzed using Cox regression models. Results: A total of 39 patients were included in the present study. Nuclear MSH2 staining was statistically significantly correlated to Ki-67 expression (p=0.022). However, univariate and multivariate Cox analyses showed no statistically significant association between the expression of the investigated biomarkers and overall survival or relapse. Conclusion: The present exploratory study does not show any significant predictive value of the biomarkers examined with respect to survival or relapse. However, with larger patient cohorts, we believe that protein profiling using TMAs and immunohistochemistry is a feasible strategy for prognostic and predictive biomarker screening in laryngeal cancer.
|
|
| 2. |
- Holst, Anders, et al.
(författare)
-
Statistical anomaly detection for train fleets
- 2013
-
Ingår i: The AI Magazine. - : Wiley. - 0738-4602 .- 2371-9621. ; 34:1, s. 33-42
-
Tidskriftsartikel (refereegranskat)abstract
- We have developed a method for statistical anomaly detection that has been deployed in a tool for condition monitoring of train fleets. The tool is currently used by several railway operators across the world to inspect and visualize the occurrence of "event messages" generated on the trains. The anomaly detection component helps the operators quickly to find significant deviations from normal behavior and to detect early indications for possible problems. The method used is based on Bayesian principal anomaly, which is a framework for parametric anomaly detection using Bayesian statistics. The savings in maintenance costs of using the tool comes mainly from avoiding costly breakdowns and have been estimated to be several million Euros per year for the tool. In the long run, it is expected that maintenance costs can be reduced by between 5 and 10 percent with the help of the tool.
|
|
| 3. |
- Neumann, W. P., et al.
(författare)
-
Ergonomics action research I : shifting from hypothesis testing to experiential learning
- 2012
-
Ingår i: Ergonomics. - : Informa UK Limited. - 0014-0139 .- 1366-5847. ; 55:10, s. 1127-1139
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents the case for the need for 'Action Research' (AR) approaches to gain understanding of how ergonomics considerations can best be integrated into the design of new work systems. The AR researchers work collaboratively with other stakeholders to solve a real-world problem: gaining insight into the problem and factors influencing solution building from an embedded position in the development process. This experience is interpreted in terms of available theory and can support further theory development. This non-experimental approach can help provide practical new approaches for integrating ergonomics considerations into real work system design processes. The AR approach suffers from a lack of acceptance by conventionally trained scientists. This paper aims to help overcome this weakness by developing the underlying theory and rationale for using AR approaches in ergonomics research. We propose further development of hybrid approaches which incorporate other evaluation techniques to extend the knowledge gains from AR projects.
|
|
