Molecular Mapping of Neutral Lipids using Silicon Nanopost Arrays and TIMS Imaging Mass Spectrometry

<p>Our work demonstrates the ability to perform high spatial resolution imaging mass spectrometry (IMS) using silicon nanopost arrays (NAPA), a matrix-free laser desorption ionization (LDI) substrate, for multiple classes of neutral lipids from biological tissue sections.</p> <p><br></p><p>Utilized trapped ion-mobility spectrometry (TIMS) coupled with IMS to enhance the molecular coverage of detected neutral lipid species, as well as for differentiating neutral lipid isomeric species spatial distributions. </p>

Molecular Mapping of Neutral Lipids using Silicon Nanopost Arrays and TIMS Imaging Mass Spectrometry

<p>Our work demonstrates the ability to perform high spatial resolution imaging mass spectrometry (IMS) using silicon nanopost arrays (NAPA), a matrix-free laser desorption ionization (LDI) substrate, for multiple classes of neutral lipids from biological tissue sections.</p> <p><br></p><p>Utilized trapped ion-mobility spectrometry (TIMS) coupled with IMS to enhance the molecular coverage of detected neutral lipid species, as well as for differentiating neutral lipid isomeric species spatial distributions. </p>

Impact of Tree Crown Transmittance on Surface Reflectance Retrieval in the Shade for High Spatial Resolution Imaging Spectroscopy: A Simulation Analysis Based on Tree Modeling Scenarios

With the advancement of high spatial resolution imaging spectroscopy, an accurate surface reflectance retrieval is needed to derive relevant physical variables for land cover mapping, soil, and vegetation monitoring. One challenge is to deal with tree shadows using atmospheric correction models if the tree crown transmittance Tc is not properly taken into account. This requires knowledge of the complex radiation mechanisms that occur in tree crowns, which can be provided by coupling the physical modeling of canopy radiative transfer codes (here DART) and the 3D representations of trees. First in this study, a sensitivity analysis carried out on DART simulations with an empirical 3D tree model led to a statistical regression predicting Tc from the tree leaf area index (LAI) and the solar zenith angle with good performances (RMSE ≤ 4.3% and R2 ≥ 0.91 for LAI ≤ 4 m2.m−2). Secondly, more realistic 3D voxel-grid tree models derived from terrestrial LiDAR measurements over two trees were considered. The comparison of DART-simulated Tc from these models with the previous predicted Tc over 0.4–2.5 µm showed three main sources of inaccuracy quoted in order of importance: (1) the global tree geometry shape (mean bias up to 21.5%), (2) the transmittance fraction associated to multiple scattering, Tscat (maximum bias up to 13%), and (3) the degree of realism of the tree representation (mean bias up to 7.5%). Results showed that neglecting Tc leads to very inaccurate reflectance retrieval (mean bias > 0.04), particularly if the background reflectance is high, and in the near and shortwave infrared – NIR and SWIR – due to Tscat. The transmittance fraction associated to the non-intercepted transmitted light, Tdir, can reach up to 95% in the SWIR, and Tscat up to 20% in the NIR. Their spatial contributions computed in the tree shadow have a maximum dispersion of 27% and 8% respectively. Investigating how to approximate Tdir and Tscat spectral and spatial variability along with the most appropriate tree 3D modeling is crucial to improve reflectance retrieval in tree shadows when using atmospheric correction models.

High spatial-resolution imaging of label-free in vivo protein aggregates by VISTA

Amyloid aggregation, formed by aberrant proteins, is a pathological hallmark for neurodegenerative diseases, including Alzheimer’s disease and Huntington’s disease. High-resolution holistic mapping of the fine structures from these aggregates should...