ALBERT

Description: State-of-the-art deep learning technology has been successfully applied to relatively small selected areas of very high spatial resolution (0.15 and 0.25 m) optical aerial imagery acquired by a fixed-wing aircraft to automatically characterize ice-wedge polygons (IWPs) in the Arctic tundra. However, any mapping of IWPs at regional to continental scales requires images acquired on different sensor platforms (particularly satellite) and a refined understanding of the performance stability of the method across sensor platforms through reliable evaluation assessments. In this study, we examined the transferability of a deep learning Mask Region-Based Convolutional Neural Network (R-CNN) model for mapping IWPs in satellite remote sensing imagery (~0.5 m) covering 272 km2 and unmanned aerial vehicle (UAV) (0.02 m) imagery covering 0.32 km2. Multi-spectral images were obtained from the WorldView-2 satellite sensor and pan-sharpened to ~0.5 m, and a 20 mp CMOS sensor camera onboard a UAV, respectively. The training dataset included 25,489 and 6022 manually delineated IWPs from satellite and fixed-wing aircraft aerial imagery near the Arctic Coastal Plain, northern Alaska. Quantitative assessments showed that individual IWPs were correctly detected at up to 72% and 70%, and delineated at up to 73% and 68% F1 score accuracy levels for satellite and UAV images, respectively. Expert-based qualitative assessments showed that IWPs were correctly detected at good (40–60%) and excellent (80–100%) accuracy levels for satellite and UAV images, respectively, and delineated at excellent (80–100%) level for both images. We found that (1) regardless of spatial resolution and spectral bands, the deep learning Mask R-CNN model effectively mapped IWPs in both remote sensing satellite and UAV images; (2) the model achieved a better accuracy in detection with finer image resolution, such as UAV imagery, yet a better accuracy in delineation with coarser image resolution, such as satellite imagery; (3) increasing the number of training data with different resolutions between the training and actual application imagery does not necessarily result in better performance of the Mask R-CNN in IWPs mapping; (4) and overall, the model underestimates the total number of IWPs particularly in terms of disjoint/incomplete IWPs.

Description: Mountain-basin systems (MBS) in Central Asia are unique and complex ecosystems, wherein their elevation gradients lead to high spatial heterogeneity in vegetation and its response to climate change. Exploring elevation-dependent vegetation greenness variation and the effects of climate factors on vegetation has important theoretical and practical significance for regulating the ecological processes of this system. Based on the MODIS NDVI (remotely sensed normalized difference vegetation index), and observed precipitation and temperature data sets, we analyzed vegetation greenness and climate patterns and dynamics with respect to elevation (300–3600 m) in a typical MBS, in Altay Prefecture, China, during 2000–2017. Results showed that vegetation exhibited a greening (NDVI) trend for the whole region, as well as the mountain, oasis and desert zones, but only the desert zone reached significant level. Vegetation in all elevation bins showed greening, with significant trends at 400–700 m and 2600–3500 m. In summer, lower elevation bins (below 1500 m) had a nonsignificant wetting and warming trend and higher elevation bins had a nonsignificant drying and warming trend. Temperature trend increased with increasing elevation, indicating that warming was stronger at higher elevations. In addition, precipitation had a significantly positive coefficient and temperature a nonsignificant coefficient with NDVI at both regional scale and subregional scale. Our analysis suggests that the regional average could mask or obscure the relationship between climate and vegetation at elevational scale. Vegetation greenness had a positive response to precipitation change in all elevation bins, and had a negative response to temperature change at lower elevations (below 2600 m), and a positive response to temperature change at higher elevations. We observed that vegetation greenness was more sensitive to precipitation than to temperature at lower elevations (below 2700 m), and was more sensitive to temperature at higher elevations.

Description: Thermodynamic hydricity (HDAMeCN) determined as Gibbs free energy (ΔG°[H]−) of the H− detachment reaction in acetonitrile (MeCN) was assessed for 144 small borane clusters (up to 5 boron atoms), polyhedral closo-boranes dianions [BnHn]2−, and their lithium salts Li2[BnHn] (n = 5–17) by DFT method [M06/6-311++G(d,p)] taking into account non-specific solvent effect (SMD model). Thermodynamic hydricity values of diborane B2H6 (HDAMeCN = 82.1 kcal/mol) and its dianion [B2H6]2− (HDAMeCN = 40.9 kcal/mol for Li2[B2H6]) can be selected as border points for the range of borane clusters’ reactivity. Borane clusters with HDAMeCN below 41 kcal/mol are strong hydride donors capable of reducing CO2 (HDAMeCN = 44 kcal/mol for HCO2−), whereas those with HDAMeCN over 82 kcal/mol, predominately neutral boranes, are weak hydride donors and less prone to hydride transfer than to proton transfer (e.g., B2H6, B4H10, B5H11, etc.). The HDAMeCN values of closo-boranes are found to directly depend on the coordination number of the boron atom from which hydride detachment and stabilization of quasi-borinium cation takes place. In general, the larger the coordination number (CN) of a boron atom, the lower the value of HDAMeCN.

Description: The development of recombinant therapeutic proteins has been a major revolution in modern medicine. Therapeutic-based monoclonal antibodies (mAbs) are growing rapidly, providing a potential class of human pharmaceuticals that can improve the management of cancer, autoimmune diseases, and other conditions. Most mAbs are typically of the immunoglobulin G (IgG) subclass, and they are glycosylated at the conserved asparagine position 297 (Asn-297) in the CH2 domain of the Fc region. Post-translational modifications here account for the observed high heterogeneity of glycoforms that may or not impact the stability, pharmacokinetics (PK), efficacy, and immunogenicity of mAbs. These modifications are also critical for the Fc receptor binding, and consequently, key antibody effector functions including antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC). Moreover, mAbs produced in non-human cells express oligosaccharides that are not normally found in serum IgGs might lead to immunogenicity issues when administered to patients. This review summarizes our understanding of the terminal sugar residues, such as mannose, sialic acids, fucose, or galactose, which influence therapeutic mAbs either positively or negatively in this regard. This review also discusses mannosylation, which has significant undesirable effects on the PK of glycoproteins, causing a decreased mAbs’ half-life. Moreover, terminal galactose residues can enhance CDC activities and Fc–C1q interactions, and core fucose can decrease ADCC and Fc–FcγRs binding. To optimize the therapeutic use of mAbs, glycoengineering strategies are used to reduce glyco-heterogeneity of mAbs, increase their safety profile, and improve the therapeutic efficacy of these important reagents.

Description: Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with poor prognosis. The IPF-conditioned matrix (IPF-CM) system enables the study of matrix–fibroblast interplay. While effective at slowing fibrosis, nintedanib has limitations and the mechanism is not fully elucidated. In the current work, we explored the underlying signaling pathways and characterized nintedanib involvement in the IPF-CM fibrotic process. Results were validated using IPF patient samples and bleomycin-treated animals with/without oral and inhaled nintedanib. IPF-derived primary human lung fibroblasts (HLFs) were cultured on Matrigel and then cleared using NH4OH, creating the IPF-CM. Normal HLF-CM served as control. RNA-sequencing, PCR and western-blots were performed. HIF1α targets were evaluated by immunohistochemistry in bleomycin-treated rats with/without nintedanib and in patient samples with IPF. HLFs cultured on IPF-CM showed over-expression of ‘HIF1α signaling pathway’ (KEGG, p 〈 0.0001), with emphasis on SERPINE1 (PAI-1), VEGFA and TIMP1. IPF patient samples showed high HIF1α staining, especially in established fibrous tissue. PAI-1 was overexpressed, mainly in alveolar macrophages. Nintedanib completely reduced HIF1α upregulation in the IPF-CM and rat-bleomycin models. IPF-HLFs alter the extracellular matrix, thus creating a matrix that further propagates an IPF-like phenotype in normal HLFs. This pro-fibrotic loop includes the HIF1α pathway, which can be blocked by nintedanib.

Description: Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. We employed a systematic experiment to understand the degree of interoperability of knowledge-based workflows across distinct tundra vegetation units—sedge tundra and tussock tundra—focusing on the same semantic class. In our multi-scale trough modelling workflow, we coupled mathematical morphological filtering with a segmentation process to enhance the quality of image object candidates and classification accuracies. Employment of the master ruleset on sedge tundra reported classification accuracies of correctness of 0.99, completeness of 0.87, and F1 score of 0.92. When the master ruleset was applied to tussock tundra without any adaptations, classification accuracies remained promising while reporting correctness of 0.87, completeness of 0.77, and an F1 score of 0.81. Overall, results suggest that the object-based image analysis-based trough modelling workflow exhibits substantial interoperability across the terrain while producing promising classification accuracies. From an Arctic earth science perspective, the mapped troughs combined with the ArcticDEM can allow hydrological assessments of lateral connectivity of the rapidly changing Arctic tundra landscape, and repeated mapping can allow us to track fine-scale changes across large regions and that has potentially major implications on larger riverine systems.