It is very difficult to analyze large amounts of hyperspectral data. Here we present a method based on reducing the dimensionality of the data and clustering the result in moving toward classification of the data. Dimensionality reduction is done with diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original dataset in order to obtain an efficient representation of data geometric descriptions. Clustering is done using k-means and a neural network clustering theory, fuzzy ART. The process is done on a subset of core data from AngloGold Ashanti, and compared to results obtained by AngloGold Ashanti's proprietary method. Experimental results show that the proposed methods are promising in addressing the complicated hyperspectral data and identifying the minerals in core samples.

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ObjectiveCiliary dysfunctions may have deleterious consequences on mucociliary clearance. We propose a new approach based on coupling the isolated ciliary beat pattern and the global efficiency of ciliary beat on human ciliated cells. MethodsCiliated cells issued from nasal brushing (controls and primary ciliary dyskinesia patients) were recorded by high-speed video-microscopy (350 frames s -1 ). We have performed an original quantitative analysis of ciliary beat dynamics (CBD) by following cilium tips. It allows to describe different parameters including ciliary beat frequency also measured by Fast-Fourier-Transform and Video-Kymography. We have also developed the microbeads tracking method (MBT) to get an index of the global efficiency of ciliary beat. Here, micro-beads (4.5 µm) have been used as markers of the flow generated by beating cilia.

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