Analysis and Discussion on the Optimal Noise Model of Global GNSS Long-Term Coordinate Series Considering Hydrological Loading

The displacement of Global Navigation Satellite System (GNSS) station contains the information of surface elastic deformation caused by the variation of land water reserves. This paper selects the long-term coordinate series data of 671 International GNSS Service (IGS) reference stations distributed globally under the framework of World Geodetic System 1984 (WGS84) from 2000 to 2021. Different noise model combinations are used for noise analysis, and the optimal noise model for each station before and after hydrologic loading correction is calculated. The results show that the noise models of global IGS reference stations are diverse, and each component has different optimal noise model characteristics, mainly white noise + flicker noise (WN+FN), generalized Gauss–Markov noise (GGM) and white noise + power law noise (WN+PL). Through specific analysis between the optimal noise model and the time series velocity of the station, it is found that the maximum influence value of the vertical velocity can reach 1.8 mm when hydrological loading is considered. Different complex noise models also have a certain influence on the linear velocity and velocity uncertainty of the station. Among them, the influence of white noise + random walking noise is relatively obvious, and its maximum influence value in the elevation direction can reach over 2 mm/year. When studying the impact of hydrological loading correction on the periodicity of the coordinate series, it is concluded whether the hydrological loading is calculated or not, and the GNSS long-term coordinate series has obvious annual and semi-annual amplitude changes, which are most obvious in the vertical direction, up to 16.48 mm

Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity

Abstract Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types

Analysis and Discussion on the Optimal Noise Model of Global GNSS Long-Term Coordinate Series Considering Hydrological Loading

The displacement of Global Navigation Satellite System (GNSS) station contains the information of surface elastic deformation caused by the variation of land water reserves. This paper selects the long-term coordinate series data of 671 International GNSS Service (IGS) reference stations distributed globally under the framework of World Geodetic System 1984 (WGS84) from 2000 to 2021. Different noise model combinations are used for noise analysis, and the optimal noise model for each station before and after hydrologic loading correction is calculated. The results show that the noise models of global IGS reference stations are diverse, and each component has different optimal noise model characteristics, mainly white noise + flicker noise (WN+FN), generalized Gauss–Markov noise (GGM) and white noise + power law noise (WN+PL). Through specific analysis between the optimal noise model and the time series velocity of the station, it is found that the maximum influence value of the vertical velocity can reach 1.8 mm when hydrological loading is considered. Different complex noise models also have a certain influence on the linear velocity and velocity uncertainty of the station. Among them, the influence of white noise + random walking noise is relatively obvious, and its maximum influence value in the elevation direction can reach over 2 mm/year. When studying the impact of hydrological loading correction on the periodicity of the coordinate series, it is concluded whether the hydrological loading is calculated or not, and the GNSS long-term coordinate series has obvious annual and semi-annual amplitude changes, which are most obvious in the vertical direction, up to 16.48 mm

Static and dynamic impacts of venture capital shareholding on stock price crash risk

With the rapid development of venture capital industry in China, venture capital firms are playing an increasingly active role in funding startups. As more and more companies with venture capital shareholders go public, venture capital firms become an important group of institutional investors that can bring significant influence on companies’ stock price. In the past decade, extremely high exit returns from IPO of venture capital firms raised the public and Chinese scholars’ attention. However, few researches inspected the venture capital’s post-IPO influence. In this paper, we examine whether venture capital shareholding will lead to companies’ higher stock crash risk, and whether different venture capital shareholders’ different behavior of holding and selling after lockup period will have different impacts on companies’ crash risk. The research used descriptive statistics and regression analysis to analyze the quarterly data of Chinese A-share companies from 2005-2016. We found that: (1) Companies with venture capital shareholders in a specific quarter will have greater incentive of earnings management (exaggerating revenue and profit) and greater stock price crash risk in the next quarter then companies without venture capital shareholders. (2) Venture capital shareholders continuing to hold non-restricted shares after lockup period is considered as a positive signal, thus reduce the crash risk. Venture capital shareholders selling shares is considered as a negative signal, thus increase the crash risk

Sequential Ambiguity Resolution Method for Poorly-Observed GNSS Data

Integer ambiguity resolution is required to obtain precise coordinates for the global navigation satellite system (GNSS). Poorly observed data cause unfixed integer ambiguity and reduce the coordinate accuracy. Previous studies mostly used denoise filters and partial ambiguity resolution algorithms to address this problem. This study proposes a sequential ambiguity resolution method that includes a float solution substitution process and a double-difference (DD) iterative correction equation process. The float solution substitution process updates the initial float solution, while the DD iterative correction equation process is used to eliminate the residual biases. The satellite-selection experiment shows that the float solution substitution process is adequate to obtain a more accurate float solution. The iteration-correction experiment shows that the double-difference iterative correction equation process is feasible with an improvement in the ambiguity success rate from 28.4% to 96.2%. The superiority experiment shows significant improvement in the ambiguity success rate from 36.1% to 83.6% and a better baseline difference from about 0.1 m to 0.04 m. It is proved that the proposed sequential ambiguity resolution method can significantly optimize the results for poorly-observed GNSS data

Loose yarn of Ag-ZnO-PAN/ITO hybrid nanofibres : preparation, characterization and antibacterial evaluation

Silver nanoparticles-attached zinc oxide (ZnO) nanorods on polyacrylonitrile/indium tin oxide (Ag-ZnO-PAN/ITO) nanofibres with three-dimensional (3D) hierarchical nanostructures were facilely prepared via electrospinning followed by hydrothermal synthesis of ZnO nanorods and silver attachment with the assistance of polydopamine (PDA) thin layer. ZnO nanorods with different dimensions and silver nanoparticles with different loadings were introduced. The morphologies and structures of the nanostructures were studied by scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray diffraction (XRD) and thermogravimetric analyzer (TGA). The effect of Ag-ZnO-PAN/ITO nanofibres with different loadings of ZnO nanorods and silver nanoparticles on their antibacterial activity against Escherichia coli and Pseudomonas aeruginosa was investigated. It is shown that the hybrid nanofibres exhibit excellent antibacterial capability. The hierarchical structure benefits the attachment of a large amount of silver nanoparticles due to its large surface area, and the deposited PDA thin layer helps improve the structure stability for long-term antibacterial application through firmly anchoring silver nanoparticles onto the surface. This work offers an effective and straightforward “all-solution” strategy to incorporate Ag-ZnO combination into nanofibrous PAN. Moreover, the hierarchical nanostructure integrates the antibacterial function of both ZnO and silver, and renders itself a promising candidate as for sportswear and equipment

Domain Adaptation on Point Clouds via Geometry-Aware Implicits

As a popular geometric representation, point clouds have attracted much attention in 3D vision, leading to many applications in autonomous driving and robotics. One important yet unsolved issue for learning on point cloud is that point clouds of the same object can have significant geometric variations if generated using different procedures or captured using different sensors. These inconsistencies induce domain gaps such that neural networks trained on one domain may fail to generalize on others. A typical technique to reduce the domain gap is to perform adversarial training so that point clouds in the feature space can align. However, adversarial training is easy to fall into degenerated local minima, resulting in negative adaptation gains. Here we propose a simple yet effective method for unsupervised domain adaptation on point clouds by employing a self-supervised task of learning geometry-aware implicits, which plays two critical roles in one shot. First, the geometric information in the point clouds is preserved through the implicit representations for downstream tasks. More importantly, the domain-specific variations can be effectively learned away in the implicit space. We also propose an adaptive strategy to compute unsigned distance fields for arbitrary point clouds due to the lack of shape models in practice. When combined with a task loss, the proposed outperforms state-of-the-art unsupervised domain adaptation methods that rely on adversarial domain alignment and more complicated self-supervised tasks. Our method is evaluated on both PointDA-10 and GraspNet datasets. The code and trained models will be publicly available

Comparative Analysis of the Effect of the Loading Series from GFZ and EOST on Long-Term GPS Height Time Series

In order to investigate the effect of different loading models on the nonlinear variations in Global Positioning System (GPS) height time series, the characteristics of annual signals (amplitude and phase) of GPS time series, loading series from Deutsche GeoForschungsZentrum, Germany (GFZ) and School and Observatory of Earth Sciences, France (EOST) at 633 global GPS stations are processed and analyzed. The change characteristics of the root mean square (RMS) reduction rate, annual amplitude and phase of GPS time series after environmental loading corrections (ELCs) are then detected. Results show that ELCs have a positive effect on the reduction in the nonlinear deformation contained in most GPS stations around the world. RMS reduction rates are positive at 82.6% stations after GFZ correction and 87.4% after EOST correction, and the average reduction rates of all stations are 10.6% and 15.4%, respectively. As for the environmental loading series from GFZ and EOST, their average annual amplitudes are 2.7 and 3.1 mm, which explains ~40% annual amplitude of GPS height time series (7.2 mm). Further analysis of some specific stations indicates that the annual phase difference between GPS height time series and the environmental loading series is an important reason that affects the reduction rates of the RMS and annual amplitude. The linear relationship between the annual phase difference and the annual amplitude reduction rate is significant. The linear fitting results show that when there is no annual phase difference between GPS and loading series, the reduction rates of the RMS and annual amplitude will increase to the maximum of 15.6% and 41.6% for GFZ, and 22.0% and 46.6% for EOST

Structural characterisation of algae Costaria costata fucoidan and its effects on CCl4-induced liver injury

Fucoidan is a well-known natural product that is commonly found in brown algae and shows a variety of activities, including immunomodulation, antioxidation, and the combat of carcinogens. The fucoidan fractions of Costaria costata, a brown algae introduced from Japan and cultured in northern China, were studied. The fucoidan fractions were extracted, separated, and purified using a combinatorial procedure consisting of enzymolysis, ethanol precipitation, and DEAE and size-exclusion chromatographies. The fundamental characteristics of the four enriched fucoidan fractions (F1-F4), such as their sulphate content and monosaccharide composition, were investigated. FTIR and NMR spectroscopy were employed to further elucidate the structural features of the four fractions. It was found that the F1-F4 fractions all showed oxidative activity against hydroxyl radicals. The bioactive effects of the fucoidan fractions on CCl4-induced liver injury suggest their potential use as ingredients for functional foods or pharmaceuticals. (C) 2014 Elsevier Ltd. All rights reserved

Structural Characteristics of Heparin Binding to SARS-CoV-2 Spike Protein RBD of Omicron Sub-Lineages BA.2.12.1, BA.4 and BA.5

The now prevalent Omicron variant and its subvariants/sub-lineages have led to a significant increase in COVID-19 cases and raised serious concerns about increased risk of infectivity, immune evasion, and reinfection. Heparan sulfate (HS), located on the surface of host cells, plays an important role as a co-receptor for virus–host cell interaction. The ability of heparin and HS to compete for binding of the SARS-CoV-2 spike (S) protein to cell surface HS illustrates the therapeutic potential of agents targeting protein–glycan interactions. In the current study, phylogenetic tree of variants and mutations in S protein receptor-binding domain (RBD) of Omicron BA.2.12.1, BA.4 and BA.5 were described. The binding affinity of Omicron S protein RBD to heparin was further investigated by surface plasmon resonance (SPR). Solution competition studies on the inhibitory activity of heparin oligosaccharides and desulfated heparins at different sites on S protein RBD–heparin interactions revealed that different sub-lineages tend to bind heparin with different chain lengths and sulfation patterns. Furthermore, blind docking experiments showed the contribution of basic amino acid residues in RBD and sulfo groups and carboxyl groups on heparin to the interaction. Finally, pentosan polysulfate and mucopolysaccharide polysulfate were evaluated for inhibition on the interaction of heparin and S protein RBD of Omicron BA.2.12.1, BA.4/BA.5, and both showed much stronger inhibition than heparin