Identification of the High Mannose N-Glycan Isomers Undescribed by Conventional Multicellular Eukaryotic Biosynthetic Pathways.

N-linked glycosylation is one of the most important post-translational modifications of proteins. Current knowledge of multicellular eukaryote N-glycan biosynthesis suggests high mannose N-glycans are generated in the endoplasmic reticulum and Golgi apparatus through conserved biosynthetic pathways. According to conventional biosynthetic pathways, four Man7GlcNAc2 isomers, three Man6GlcNAc2 isomers, and one Man5GlcNAc2 isomer are generated during this process. In this study, we applied our latest mass spectrometry method, logically derived sequence tandem mass spectrometry (LODES/MSn), to re-examine high mannose N-glycans extracted from various multicellular eukaryotes which are not glycosylation mutants. LODES/MSn identified many high mannose N-glycan isomers previously unreported in plantae, animalia, cancer cells, and fungi. A database consisting of retention time and CID MSn mass spectra was constructed for all possible MannGlcNAc2 (n = 5, 6, 7) isomers that include the isomers by removing arbitrary numbers and positions of mannose from canonical N-glycan, Man9GlcNAc2. Many N-glycans in this database are not found in current N-glycan mass spectrum libraries. The database is useful for rapid high mannose N-glycan isomeric identification.

Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California.

California's Central Valley, one of the most agriculturally productive regions, is also one of the most stressed aquifers in the world due to anthropogenic groundwater over-extraction primarily for irrigation. Groundwater depletion is further exacerbated by climate-driven droughts. Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry has demonstrated the feasibility of quantifying global groundwater storage changes at uniform monthly sampling, though at a coarse resolution and is thus impractical for effective water resources management. Here, we employ the Random Forest machine learning algorithm to establish empirical relationships between GRACE-derived groundwater storage and in situ groundwater level variations over the Central Valley during 2002-2016 and achieved spatial downscaling of GRACE-observed groundwater storage changes from a few hundred km to 5 km. Validations of our modeled groundwater level with in situ groundwater level indicate excellent Nash-Sutcliffe Efficiency coefficients ranging from 0.94 to 0.97. In addition, the secular components of modeled groundwater show good agreements with those of vertical displacements observed by GPS, and CryoSat-2 radar altimetry measurements and is perfectly consistent with findings from previous studies. Our estimated groundwater loss is about 30 km3 from 2002 to 2016, which also agrees well with previous studies in Central Valley. We find the maximum groundwater storage loss rates of -5.7 ± 1.2 km3 yr-1 and -9.8 ± 1.7 km3 yr-1 occurred during the extended drought periods of January 2007-December 2009, and October 2011-September 2015, respectively while Central Valley also experienced groundwater recharges during prolonged flood episodes. The 5-km resolution Central Valley-wide groundwater storage trends reveal that groundwater depletion occurs mostly in southern San Joaquin Valley collocated with severe land subsidence due to aquifer compaction from excessive groundwater over withdrawal.

Application of Biomineralization Technology to Self-Healing of Fiber-Reinforced Lightweight Concrete after Exposure to High Temperatures.

In the field of civil engineering, concrete self-healing technology plays an important role. Concrete self-healing should be able to effectively heal cracks, not only improving the internal structure, but also improving the mechanical properties and durability of the concrete structure. The biomineralization-repair method is characterized by its potential for long-lasting, rapid, and active crack repair potential. Biomineralization repair has an effective bond ability, is compatible with concrete components, and is also environmentally friendly. This study used biomineralization to explore the self-healing of fiber-reinforced lightweight concrete after its exposure to high temperatures. Concrete specimens of a control group (using lightweight aggregate without bacterial spores and a nutrient source) and an experimental group (using lightweight aggregate containing bacterial spores and a nutrient source) were prepared. The repair effect of the microbial self-healing concrete after the exposure to high temperature was observed by a crack-width gauge, field-emission scanning electron microscopy (FESEM), energy-dispersive spectroscopy (EDS), and X-ray diffraction (XRD). According to the EDS and XRD analyses, the precipitate formed at the crack was calcium carbonate. After 28 days of self-healing, the water absorption rate of the experimental group was lower than that of the control group. This is because the specimens of the penetration test were taken from the middle of the concrete cylinder after high temperature, and their bacterial survival rate was higher, which made the mineralization more significant. However, the mechanical test results of the control and experimental groups after the self-healing in the water were not substantially different, which indicated that the bacterial mineralization in the experimental group was slow in the absence of an adequate source of nutrients.

A consistent Great Lakes ice cover digital data set for winters 1973-2019.

Ice formation and loss in the Laurentian Great Lakes has a strong impact on regional climate, weather, economy and ecology in North America. To record the ice changes during the winter season, Great Lakes ice cover data has been collected and maintained since 1973 by Canadian Ice Service, U.S. National Ice Center, and National Oceanic and Atmospheric Administration's Great Lakes Environmental Research Laboratory. Throughout this long history, technology has improved and the needs of users have evolved, so Great Lakes ice cover datasets have been upgraded several times in both spatial and temporal resolutions. In order to make those long-term data consistent and accessible, we reprocessed the Great Lakes ice cover database to generate daily gridded data (1.8 km resolution) using a re-project method with Nearest Neighbor Search for spatial interpolation, and linear interpolation with categorization for temporal interpolation. This report elucidates data history, generation procedures, and file structure in order to improve access and usability of Great Lakes ice cover data.