2,647 research outputs found
Complementarity of PALM and SOFI for super-resolution live cell imaging of focal adhesions
Live cell imaging of focal adhesions requires a sufficiently high temporal
resolution, which remains a challenging task for super-resolution microscopy.
We have addressed this important issue by combining photo-activated
localization microscopy (PALM) with super-resolution optical fluctuation
imaging (SOFI). Using simulations and fixed cell focal adhesion images, we
investigated the complementarity between PALM and SOFI in terms of spatial and
temporal resolution. This PALM-SOFI framework was used to image focal adhesions
in living cells, while obtaining a temporal resolution below 10 s. We
visualized the dynamics of focal adhesions, and revealed local mean velocities
around 190 nm per minute. The complementarity of PALM and SOFI was assessed in
detail with a methodology that integrates a quantitative resolution and
signal-to-noise metric. This PALM and SOFI concept provides an enlarged
quantitative imaging framework, allowing unprecedented functional exploration
of focal adhesions through the estimation of molecular parameters such as the
fluorophore density and the photo-activation and photo-switching rates
On mining complex sequential data by means of FCA and pattern structures
Nowadays data sets are available in very complex and heterogeneous ways.
Mining of such data collections is essential to support many real-world
applications ranging from healthcare to marketing. In this work, we focus on
the analysis of "complex" sequential data by means of interesting sequential
patterns. We approach the problem using the elegant mathematical framework of
Formal Concept Analysis (FCA) and its extension based on "pattern structures".
Pattern structures are used for mining complex data (such as sequences or
graphs) and are based on a subsumption operation, which in our case is defined
with respect to the partial order on sequences. We show how pattern structures
along with projections (i.e., a data reduction of sequential structures), are
able to enumerate more meaningful patterns and increase the computing
efficiency of the approach. Finally, we show the applicability of the presented
method for discovering and analyzing interesting patient patterns from a French
healthcare data set on cancer. The quantitative and qualitative results (with
annotations and analysis from a physician) are reported in this use case which
is the main motivation for this work.
Keywords: data mining; formal concept analysis; pattern structures;
projections; sequences; sequential data.Comment: An accepted publication in International Journal of General Systems.
The paper is created in the wake of the conference on Concept Lattice and
their Applications (CLA'2013). 27 pages, 9 figures, 3 table
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