Benthic Habitat Quality Assessment in Estuarine Intertidal Flats Based on Long-Term Data with Focus on Responses to Eco-Restoration Activity

A long-term assessment of the benthic habitat quality of intertidal flats in Liaohe Estuary was conducted by three integrating ecological indices, AZTI’s Marine Biotic Index (AMBI), Multivariate-AMBI (M-AMBI), and Shannon–Wiener diversity index (H′) based on macrobenthos data from 2013 to 2020. The results showed that the macrobenthic communities were characterized by indifferent and sensitive species of AMBI ecological groups. The annual ranges of H′, AMBI, and M-AMBI were 0.77–1.56, 1.44–3.73 and 0.36–0.54, respectively. Noticeable differences were found among assessment obtained by these biotic indices. Approximately 100%, 24%, and 78% sampling sites had “moderate”, “poor”, and “bad” statuses as assessed by H′, AMBI, and M-AMBI, respectively. Compared with H′ and AMBI, M-AMBI may be more applicable to evaluate the benthic habitat quality of intertidal flats in Liaohe Estuary. Results suggest that the benthic habitat quality in the middle parts of intertidal flats still had an unacceptable status and has not improved radically to date after large-scale “mariculture ponds restored to intertidal flats”.publishedVersio

Strain Distribution of Au and Ag Nanoparticles Embedded in Al 2

Au and Ag nanoparticles embedded in amorphous Al2O3 matrix are fabricated by the pulsed laser deposition (PLD) method and rapid thermal annealing (RTA) technique, which are confirmed by the experimental high-resolution transmission electron microscope (HRTEM) results, respectively. The strain distribution of Au and Ag nanoparticles embedded in the Al2O3 matrix is investigated by the finite-element (FE) calculations. The simulation results clearly indicate that both the Au and Ag nanoparticles incur compressive strain by the Al2O3 matrix. However, the compressive strain existing on the Au nanoparticle is much weaker than that on the Ag nanoparticle. This phenomenon can be attributed to the reason that Young’s modulus of Au is larger than that of Ag. This different strain distribution of Au and Ag nanoparticles in the same host matrix may have a significant influence on the technological potential applications of the Au-Ag alloy nanoparticles

Apolipoprotein E lipoprotein particles inhibit amyloid-β uptake through cell surface heparan sulphate proteoglycan

Binding affinity of heparin-apoE3 interaction. (A) Representative dot blot of heparin and apoE3 particles. Heparin was spotted onto nitrocellulose membrane along with mouse monoclonal anti-apoE antibody, WUE4, as a positive control and normal mouse IgG as a background. Membrane strips were incubated with increasing concentrations of apoE3 particles from immortalized astrocytes. Membrane-bound apoE was then visualized by biotin-conjugate anti-apoE antibody and infrared streptavidin secondary antibody. (B) Integrated infrared signal intensities from each dot were obtained and the average intensities from three independent experiments were plotted to acquire binding affinity curve and the dissociation constant (Kd). (TIF 2432 kb

Establishment of a diagnostic model based on immune-related genes in children with asthma

Objective: Allergic asthma is driven by an antigen-specific immune response. This study aimed to identify immune-related differentially expressed genes in childhood asthma and establish a classification diagnostic model based on these genes. Methods: GSE65204 and GSE19187 were downloaded and served as training set and validation set. The immune cell composition was evaluated with ssGSEA algorithm based on the immune-related gene set. Modules that significantly related to the asthma were selected by WGCNA algorithm. The immune-related differentially expressed genes (DE-IRGs) were screened, the protein-protein interaction network and diagnostic model of DE-IRGs was constructed. The pathway and immune correlation analysis of hub DE-IRGs was analyzed. Results: Eight immune cell types exhibited varying levels of abundance between the asthma and control groups. A total of 112 differentially expressed immune-related genes (DE-IRGs) was identified. Through the application of four ranking methods (MCC, MNC, DEGREE, and EPC), 17 hub DE-IRGs with overlapping significance were further selected. Subsequently, 8 optimized were identified using univariate logistic regression analysis and the LASSO regression algorithm, based on which a robust diagnostic model was constructed. Notably, TNF and CD40LG emerged as direct participants in asthma-related signaling pathways, displaying a positive correlation with the immune cell types of immature B cells, activated B cells, activated CD8 T cells, activated CD4 T cells, and myeloid-derived suppressor cells. Conclusion: The diagnostic model constructed using the DE-IRGs (CCL5, CCR5, CD40LG, CD8A, IL2RB, PDCD1, TNF, and ZAP70) exhibited high and specific diagnostic value for childhood asthma. The diagnostic model may contribute to the diagnosis of childhood asthma

Shell Thickness-Dependent Strain Distributions of Confined Au/Ag and Ag/Au Core-Shell Nanoparticles

The shell thickness-dependent strain distributions of the Au/Ag and Ag/Au core-shell nanoparticles embedded in Al2O3 matrix have been investigated by finite element method (FEM) calculations, respectively. The simulation results clearly indicate that there is a substantial strain applied on both the Au/Ag and Ag/Au core-shell nanoparticles by the Al2O3 matrix. For the Au/Ag nanoparticles, it can be found that the compressive strain existing in the shell is stronger than that on the center of core and reaches the maximum at the interface between the shell and core. In contrast, for the Ag/Au nanoparticles, the compressive strain applied on the core is much stronger than that at the interface and that in the shell. With the shell thickness increasing, both of the strains in the Au/Ag and Ag/Au nanoparticles increase as well. However, the strain gradient in the shell decreases gradually with the increasing of the shell thickness for both of Ag/Au ad Au/Ag nanoparticles. These results provide an effective method to manipulate the strain distributions of the Au/Ag and Ag/Au nanoparticles by tuning the thickness of the shell, which can further have significant influences on the microstructures and physical properties of Au/Ag and Ag/Au nanoparticles

Long-Term Ice Conditions in Yingkou, a Coastal Region Northeast of the Bohai Sea, between 1951/1952 and 2017/2018: Modeling and Observations

Bohai Sea ice creates obstacles for maritime navigation and offshore activities. A better understanding of ice conditions is valuable for sea-ice management. The evolution of 67 years of seasonal ice thickness in a coastal region (Yingkou) in the Northeast Bohai Sea was simulated by using a snow/ice thermodynamic model, using local weather-station data. The model was first validated by using seasonal ice observations from field campaigns and a coastal radar (the season of 2017/2018). The model simulated seasonal ice evolution well, particularly ice growth. We found that the winter seasonal mean air temperature in Yingkou increased by 0.33 °C/decade slightly higher than air temperature increase (0.27 °C/decade) around Bohai Sea. The decreasing wind-speed trend (0.05 m/s perdecade) was a lot weaker than that averaged (0.3 m/s per decade) between the early 1970s and 2010s around the entire Bohai Sea. The multi-decadal ice-mass balance revealed decreasing trends of the maximum and average ice thickness of 2.6 and 0.8 cm/decade, respectively. The length of the ice season was shortened by 3.7 days/decade, and ice breakup dates were advanced by 2.3 days/decade. All trends were statistically significant. The modeled seasonal maximum ice thickness is highly correlated (0.83, p < 0.001) with the Bohai Sea Ice Index (BoSI) used to quantify the severity of the Bohai Sea ice condition. The freezing-up date, however, showed a large interannual variation without a clear trend. The simulations indicated that Bohai ice thickness has grown continuously thinner since 1951/1952. The time to reach 0.15 m level ice was delayed from 3 January to 21 January, and the ending time advanced from 6 March to 19 February. There was a significant weakening of ice conditions in the 1990s, followed by some recovery in 2000s. The relationship between large-scale climate indices and ice condition suggested that the AO and NAO are strongly correlated with interannual changes in sea-ice thickness in the Yingkou region

Prediction of Forest Structural Parameters Using Airborne Full-Waveform LiDAR and Hyperspectral Data in Subtropical Forests

Accurate acquisition of forest structural parameters, which is essential for the parameterization of forest growth models and understanding forest ecosystems, is also crucial for forest inventories and sustainable forest management. In this study, simultaneously acquired airborne full-waveform (FWF) LiDAR and hyperspectral data were used to predict forest structural parameters in subtropical forests of southeast China. The pulse amplitude and waveform shape of airborne FWF LiDAR data were calibrated using a physical process-driven and a voxel-based approach, respectively. Different suites of FWF LiDAR and hyperspectral metrics, i.e., point cloud (derived from LiDAR-waveforms) metrics (DPC), full-waveform (geometric and radiometric features) metrics (FW) and hyperspectral (original reflectance bands, vegetation indices and statistical indices) metrics (HS), were extracted and assessed using correlation analysis and principal component analysis (PCA). The selected metrics of DPC, FW and HS were used to fit regression models individually and in combination to predict diameter at breast height (DBH), Lorey&#8217;s mean height (HL), stem number (N), basal area (G), volume (V) and above ground biomass (AGB), and the capability of the predictive models and synergetic effects of metrics were assessed using leave-one-out cross validation. The results showed that: among the metrics selected from three groups divided by the PCA analysis, twelve DPC, eight FW and ten HS were highly correlated with the first and second principal component (r &gt; 0.7); most of the metrics selected from DPC, FW and HS had weak relationships between each other (r &lt; 0.7); the prediction of HL had a relatively higher accuracy (Adjusted-R2 = 0.88, relative RMSE = 10.68%), followed by the prediction of AGB (Adjusted-R2 = 0.84, relative RMSE = 15.14%), and the prediction of V had a relatively lower accuracy (Adjusted-R2 = 0.81, relative RMSE = 16.37%); and the models including only DPC had the capability to predict forest structural parameters with relatively high accuracies (Adjusted-R2 = 0.52&#8315;0.81, relative RMSE = 15.70&#8315;40.87%) whereas the usage of DPC and FW resulted in higher accuracies (Adjusted-R2 = 0.62&#8315;0.87, relative RMSE = 11.01&#8315;31.30%). Moreover, the integration of DPC, FW and HS can further improve the accuracies of forest structural parameters prediction (Adjusted-R2 = 0.68&#8315;0.88, relative RMSE = 10.68&#8315;28.67%)