Glycosaminoglycans and Glycosaminoglycan Mimetics in Cancer and Inflammation

Glycosaminoglycans (GAGs) are a class of biomolecules expressed virtually on all mammalian cells and usually covalently attached to proteins, forming proteoglycans. They are present not only on the cell surface, but also in the intracellular milieu and extracellular matrix. GAGs interact with multiple ligands, both soluble and insoluble, and modulate an important role in various physiological and pathological processes including cancer, bacterial and viral infections, inflammation, Alzheimer’s disease, and many more. Considering their involvement in multiple diseases, their use in the development of drugs has been of significant interest in both academia and industry. Many GAG-based drugs are being developed with encouraging results in animal models and clinical trials, showcasing their potential for development as therapeutics. In this review, the role GAGs play in both the development and inhibition of cancer and inflammation is presented. Further, advancements in the development of GAGs and their mimetics as anti-cancer and anti-inflammatory agents are discussed

The influence of noise sources on cross-correlation amplitudes

We use analytical examples and asymptotic forms to examine the mathematical structure and physical meaning of the seismic cross correlation measurement. We show that in general, cross correlations are not Green's functions of medium, and may be very different depending on the source distribution. The modeling of noise sources using spatial distributions as opposed to discrete collections of sources is emphasized. When stations are illuminated by spatially complex source distributions, cross correlations show arrivals at a variety of time lags, from zero to the maximum surface-wave arrival time. Here, we demonstrate the possibility of inverting for the source distribution using the energy of the full cross-correlation waveform. The interplay between the source distribution and wave attenuation in determining the functional dependence of cross correlation energies on station-pair distance is quantified. Without question, energies contain information about wave attenuation. However, the accurate interpretation of such measurements is tightly connected to the knowledge of the source distribution.Comment: 19 pages, 17 figures; Geophysical Journal Internationa

Measurements and Kernels for Source-Structure Inversions in Noise Tomography

Seismic noise cross correlations are used to image crustal structure and heterogeneity. Typically, seismic networks are only anisotropically illuminated by seismic noise, a consequence of the non-uniform distribution of sources. Here, we study the sensitivity of such a seismic network to structural heterogeneity in a 2-D setting. We compute finite-frequency cross-correlation sensitivity kernels for travel-time, waveform-energy and waveform-difference measurements. In line with expectation, wavespeed anomalies are best imaged using travel times and the source distribution using cross-correlation energies. Perturbations in attenuation and impedance are very difficult to image and reliable inferences require a high degree of certainty in the knowledge of the source distribution and wavespeed model (at least in the case of transmission tomography studied here). We perform single-step Gauss-Newton inversions for the source distribution and the wavespeed, in that order, and quantify the associated Cram\'{e}r-Rao lower bound. The inversion and uncertainty estimate are robust to errors in the source model but are sensitive to the theory used to interpret of measurements. We find that when classical source-receiver kernels are used instead of cross-correlation kernels, errors appear in the both the inversion and uncertainty estimate, systematically biasing the results. We outline a computationally tractable algorithm to account for distant sources when performing inversions.Comment: 19 pages, 12 figures, Geophysical Journal Internationa