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An emerging interest regarding nanoparticles (NPs) concerns their potential immunomodulatory and pro-inflammatory activities, as well as their impact in the circulatory system. These biological activities of NPs can be related to the intensity and type of the responses, which can raise concerns about adverse side effects and limit the biomedical applicability of these nanomaterials. Therefore, the purpose of this study was to investigate the impact of a library of cationic cellulose nanocrystals (CNCs) in the human blood and endothelial cells using cell-based assays. First, we evaluated whether the cationic CNCs would cause hemolysis and aggregation or alteration on the morphology of red blood cells (RBC). We observed that although these nanomaterials did not alter RBC morphology or cause aggregation, at 24 h exposure, a mild hemolysis was detected mainly with unmodified CNCs. Then, we analyzed the effect of various concentrations of CNCs on the cell viability of human umbilical vein endothelial cells (HUVECs) in a time-dependent manner. None of the cationic CNCs caused a dose-response decrease in the cell viability of HUVEC at 24 h or 48 h of exposure. The findings of this study, together with the immunomodulatory properties of these cationic CNCs previously published, support the development of engineered cationic CNCs for biomedical applications, in particular as vaccine nanoadjuvants. 

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent. 

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest. 

Polysaccharides have been shown to have immunomodulatory properties. Modulation of the immune system plays a crucial role in physiological processes as well as in the treatment and/or prevention of autoimmune and infectious diseases. Cellulose nanocrystals (CNCs) are derived from cellulose, the most abundant polysaccharide on the earth. CNCs are an emerging class of crystalline nanomaterials with exceptional physico-chemical properties for high-end applications and commercialization prospects. The aim of this study was to design, synthesize, and evaluate the cytotoxicity of a series of biocompatible, wood-based, cationic CNCs as potential immunomodulators. The anionic CNCs were rendered cationic by grafting with cationic polymers having pendant +NMe3 and +NH3 moieties. The success of the synthesis of the cationic CNCs was evidenced by Fourier transform infrared spectroscopy, dynamic light scattering, zeta potential, and elemental analysis. No modification in the nanocrystals rod-like shape was observed in transmission electron microscopy and atomic force microscopy analyses. Cytotoxicity studies using three different cell-based assays (MTT, Neutral Red, and LIVE/DEAD®) and three relevant mouse and human immune cells indicated very low cytotoxicity of the cationic CNCs in all tested experimental conditions. Overall, our results showed that cationic CNCs are suitable to be further investigated as immunomodulators and potential vaccine nanoadjuvants.