Sari Peltonen

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The aim of the project was to produce updated information during 2005-14 on the Fusarium species found in Finnish cereal grains, and the toxins produced by them, as the last comprehensive survey study of Fusarium species and their toxins in Finland was carried out at the turn of the 1960s and the 1970s. Another aim was to use the latest molecular and(More)
Suspension-cultured barley cells responded to treatments with crude yeast extract and purified glucan preparation by rapidly and transiently (4 h postelicitation) inducing L-phenylalanine ammonia-lyase activity. Similarly, treatment of cell cultures with chitosan resulted in increased phenylalanine ammonia-lyase activity 2–4 h after elicitation, whereas a(More)
High-resolution positron emission tomography (PET) scanners have brought many improvements to the nuclear medicine imaging field. However, the mechanical limitations in the construction of the scanners introduced gaps between the detectors, and accordingly, to the acquired projection data. When the methods requiring full-sinogram dataset, e.g., filtered(More)
When selecting a filter for an application, it is often essential to know the behaviour of the filter in presence of contamination. This robustness of a filter is traditionally explored by means of influence function (IF) and change-of-variance function (CVF). However, as these are asymptotic measures there is uncertainty of the applicability of the(More)
Practical cases where a 1-D signal is corrupted by mixed Poisson and specific impulsive noise are considered. The requirements to the filtering method are discussed. It is shown that DCT based filters combined in adaptive manner with some robust filter, e.g., a standard median filter, can effectively be applied for noise removal in the considered case.(More)
In this study, we review the sinogram domain gap-filling methods for the positron emission tomograph (PET) data. We restricted our literature search to the published methods for the diamond shaped gaps which exist in the PENN-PET (University of Pennsylvania, Philadelphia, PA, USA), the MDAPET (M. D. Anderson Cancer Center, University of Texas, Houston, TX,(More)
In this paper we study robustness of L-filters by using a recently introduced method called output distributional influence function (ODIF). Unlike the traditionally used methods, such as the influence function and the change-of-variance function, the ODIF provides information about the robustness of finite length filters. So the ODIF is not only a good(More)