Structure-Preserving and Perceptually Consistent Approach for Visualization of Mass Spectrometry Imaging Datasets.

Mass spectrometry imaging (MSI) has become an important tool for 2D profiling of biological tissues, allowing for the visualization of individual compound distributions in the sample. Based on this information, it is possible to investigate the molecular organization within any particular tissue and detect abnormal regions (such as tumor regions) and many other biologically relevant phenomena. However, the large number of compounds present in the spectra hinders the productive analysis of large MSI datasets when utilizing standard tools. The heterogeneity of samples makes exploratory visualization (a presentation of the general idea of the molecular and structural organization of the inspected tissues) challenging. Here, we explore the application of various dimensionality reduction techniques that have been used extensively in the visualization of hyperspectral images and the MSI data specifically, such as principal component analysis, independent component analysis, non-negative matrix factorization, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. Further, we propose a new approach based on a combination of structure preserving visualization with nonlinear manifold embedding of normalized spectral data. This way, we aim to preserve as much spatially overlapping signals as possible while augmenting them with information on compositional (spectral) variation. The proposed approach can be used for exploratory visualization of MSI datasets without prior deep chemical or histological knowledge of the sample. Thus, different datasets can be visually compared employing the proposed method. The proposed approach allowed for the clear visualization of the molecular layer, granular layer, and white matter in chimpanzee and macaque cerebellum slices.

Wound-healing with mechanically robust and biodegradable hydrogel fibers loaded with silver nanoparticles.

The objective of this study is to provide a novel synthetic approach for the manufacture of wound-healing materials using covalently cross-linked alginate fibers loaded with silver nanoparticles. Alginate fibers are prepared by wet-spinning in a CaCl(2) precipitation bath. Using this same approach, calcium cross-links in alginate fibers are replaced by chemical cross-links that involve hydroxyl groups for subsequent cross-linking by glutaraldehyde. The cross-linked fibers become highly swollen in aqueous solution due to the presence of carboxyl functional groups, and retain their mechanical stability in physiological fluids owing to the stabilized network of covalent bonds. Alginate fibers can then be loaded with silver ions via the ion-exchange reaction. Silver ions are reduced to yield 11 nm silver nanoparticles incorporated in the polymer gel. This method provides a convenient platform to incorporate silver nanoparticles into alginate fibers in controlled concentrations while retaining the mechanical and swelling properties of the alginate fibers. Our study suggests that the silver nanoparticles loaded fibers may be easily applied in a wound healing paradigm and promote the repair process though the promotion of fibroblast migration to the wound area, reduction of the inflammatory phase, and the increased epidermal thickness in the repaired wound area, thereby improving the overall quality and speed of healing.

Superomniphobic magnetic microtextures with remote wetting control.

Universal remote control of wetting behavior enabling the transition from a superomniphobic to an omniphilic wetting state in an external magnetic field via the alternation of reentrant curvature of a microstructured surface is demonstrated. This reconfigurable microtexture made of Ni micronails repels water, water-surfactant solutions, and practically all organic liquids, whereas it gets wetted by all of these liquids after a magnetic field pulse is applied.

Fine structure in isotopic peak distributions measured using a dynamically harmonized Fourier transform ion cyclotron resonance cell at 7 T.

The fine structure of isotopic peak clusters in mass spectra of reserpine and substance P are measured using Fourier transform ion cyclotron resonance mass spectrometry at a 7 T magnetic field. The resolved peaks in the fine structure consist of (13)C, (15)N, (17)O, (18)O, (2)H, (33)S, (34)S, and combinations of them. A recently introduced high-resolution ion cyclotron resonance cell (Nikolaev, E. N.; Boldin, I. A.; Jertz, R.; Baykut, G. J. Am. Soc. Mass Spectrom. 2011, 22, 1125-1133) is used in these experiments. The positions of the experimentally obtained fine structure peaks on the mass scale agree with the isotopic distribution simulations with ≤200 ppb error. Some deviation from the theoretical isotopic distribution is observed, less abundant peaks in the fine structure patterns are a little suppressed compared to the larger ones. We present a method for atomic composition determination using accurate mass data and fine isotopic structure of the mass spectrum. Our method combines the traditional atomic composition determination from accurate mass data by enumeration of all possible formulas within constraints defined a priori with the estimation of element coefficients from resolved isotopic structures. These estimated values allow one to narrow the search space for the composition and therefore to reduce the number of candidate formulas.