No abstract

Abstract-Generalized time-frequency representations (GTFR's) which use cone-shaped kernels for nonstationary signal analysis are presented. The cone-shaped kernels are formulated for the GTFR's to produce simultaneously good resolution in time and frequency. Specifically, for a GFTR with a cone-shaped kernel, finite time support is maintained in the time dimension along with an enhanced spectrum in the frequency dimension, and the cross-terms are smoothed out. Experimental results on simulated data and real speech showed the advantages of the GTFR's with the cone-shaped kernels through comparisons to the spectrogram and the pseudo-Wigner distribution.

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretic data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) spectra of metal-organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental spectra and shuffled, then merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra.For the first time, one-to-one material identification was achieved. The optimized model showed the highest identification accuracy of 96.7% for the Top 5 ranking among a dataset of 1012 MOFs.Neighborhood components analysis (NCA) on the experimental XRD spectra shows that the spectra from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD spectra. This CNN model trained by the data-augmentation technique would not only open numerous potential applications for identifying XRD spectra for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR.

SUMMARY Skeletal development and invasion by tumor cells depends on proteolysis of collagen by the pericellular metalloproteinase MT1-MMP. Its hemopexin-like (HPX) domain binds to collagen substrates to facilitate their digestion. Spin labeling and paramagnetic NMR detection have revealed how the HPX domain docks to collagen I-derived triple-helix. Mutations impairing triple-helical peptidase activity corroborate the interface. Saturation transfer difference NMR suggests rotational averaging around the longitudinal axis of the triple-helical peptide. Part of the interface emerges as unique and potentially targetable for selective inhibition. The triple-helix crosses the junction of blades I and II at a 45° angle to the symmetry axis of the HPX domain, placing the scissile Gly~Ile bond near the HPX domain and shifted ~25 Å from MMP-1 complexes. This raises the question of the MT1-MMP catalytic domain folding over the triple-helix during catalysis, a possibility accommodated by the flexibility between domains suggested by AFM images.