The world's first wiki where authorship really matters (Nature Genetics, 2008). Due credit and reputation for authors. Imagine a global collaborative knowledge base for original thoughts. Search thousands of articles and collaborate with scientists around the globe.

wikigene or wiki gene protein drug chemical gene disease author authorship tracking collaborative publishing evolutionary knowledge reputation system wiki2.0 global collaboration genes proteins drugs chemicals diseases compound
Hoffmann, R. A wiki for the life sciences where authorship matters. Nature Genetics (2008)

SIMPLISMA and ALS applied to two-way nonlinear wavelet compressed ion mobility spectra of chemical warfare agent simulants.

Ion mobility spectrometry is a rapid scanning measurement method for which compression methods that facilitate the handling of large collections of data are beneficial. Peak distortion in reconstructed ion mobility spectra from linear wavelet compression is problematic in that artifact peaks may cause false positive alarms. Peak shifting also may cause false alarms if target peaks shift out of or interfering peaks shift into detection windows. Nonlinear wavelet compression (NLWC) preserves peak shape and can lessen the degree of distortion, shifting, and artifact peaks in the reconstructed spectra. NLWC was applied to achieve high compression and fidelity in the reconstructed spectra. Another benefit is that NLWC improves signal-to-noise ratios and thus the models built from compressed data are improved. By compressing both the drift time order and the spectrum acquisition order, greater compressions maybe achieved. A two-way nonlinear wavelet compression method that incorporates alternating least squares (2W-NLWC-ALS) algorithm was devised by applying ALS to partially reconstructed wavelet coefficients generated from two-way NLWC. The number of components in a data set can be determined automatically using ASIMPLISMA. The smaller ALS models are saved as the final compressed data and can be used to reconstruct the entire data set efficiently without maintaining the compressed wavelet coefficient matrix of the original data set. The 2W-NLWC-ALS algorithm provides greater compression ratios compared to regular wavelet compression and interpretable models. Using this method, large volumes of data can be acquired and easily evaluated through a simple compressed model. A compression ratio of 510 ppm, root-mean-square error (E(RMS)) of 6.3 mV (full-scale signal is usually 1 V or larger), and relative root-mean-square error (RE(RMS)) of 1.62% were achieved for data sets collected by CAM. A compression ratio of 46 ppm, E(RMS) of 9.2 mV, and RE(RMS) of 0.42% were achieved for data sets collected with an ITEMISER instrument. The 2W-NLWC-ALS algorithm is an efficient compression method that provides the benefits of a simple model.[1]


WikiGenes - Universities