Plant Sedimentary Ancient DNA From Far East Russia Covering the Last 28,000 Years Reveals Different Assembly Rules in Cold and Warm Climates

Woody plants are expanding into the Arctic in response to the warming climate. The impact on arctic plant communities is not well understood due to the limited knowledge about plant assembly rules. Records of past plant diversity over long time series are rare. Here, we applied sedimentary ancient DNA metabarcoding targeting the P6 loop of the chloroplast trnL gene to a sediment record from Lake Ilirney (central Chukotka, Far Eastern Russia) covering the last 28 thousand years. Our results show that forb-rich steppe-tundra and dwarf-shrub tundra dominated during the cold climate before 14 ka, while deciduous erect-shrub tundra was abundant during the warm period since 14 ka. Larix invasion during the late Holocene substantially lagged behind the likely warmest period between 10 and 6 ka, where the vegetation biomass could be highest. We reveal highest richness during 28–23 ka and a second richness peak during 13–9 ka, with both periods being accompanied by low relative abundance of shrubs. During the cold period before 14 ka, rich plant assemblages were phylogenetically clustered, suggesting low genetic divergence in the assemblages despite the great number of species. This probably originates from environmental filtering along with niche differentiation due to limited resources under harsh environmental conditions. In contrast, during the warmer period after 14 ka, rich plant assemblages were phylogenetically overdispersed. This results from a high number of species which were found to harbor high genetic divergence, likely originating from an erratic recruitment process in the course of warming. Some of our evidence may be of relevance for inferring future arctic plant assembly rules and diversity changes. By analogy to the past, we expect a lagged response of tree invasion. Plant richness might overshoot in the short term; in the long-term, however, the ongoing expansion of deciduous shrubs will eventually result in a phylogenetically more diverse community

Synthesis of Water-Soluble Iridium (III)-Containing Nanoparticles for Biological Applications

Water-soluble nanoparticles (Ir/PGlc-NP, Ir/β-1,3-glucan-NP) based on water-soluble glycopolymers (PGlc), β-1,3-glucan polysaccharide, and conjugated phosphorescent Ir (III) complexes were successfully synthesized by self-assembly. The obtained nanoparticles have good spherical morphological characterization with a mean diameter of 50 nm measured by TEM. Ir/PGlc-NP and Ir/β-1,3-glucan-NP showed the same emission maxima at 565 nm in aqueous solution and both caused effective apoptosis and death of HepG2 and Hela cells after being irradiated at 445 nm for 30 min in vitro. Fluorescence cellular imaging was conducted by confocal laser scanning microscopy (CLSM) using HepG2 cells as the model cell in which the nanoparticles had successfully entered into the cytoplasm with high brightness. Furthermore, after injecting the nanoparticles into live mice in vivo, the real-time fluorescence imaging as well as the nanoparticles distribution in organs at 24 hours after administration indicated that these nanoparticles can serve as fluorescent imaging contrast for further biological applications

Factors associated with distant metastasis in pediatric thyroid cancer: evaluation of the SEER database

Objectives: Controversies regarding factors associated with distant metastasis in pediatric thyroid cancer remain among the scientific community. The aim of this study was to investigate factors influencing distant metastasis in pediatric thyroid cancer. Methods: We reviewed 1376 patients (aged 2 to 18 years) with thyroid cancer treated between 2003 and 2014. Data collected and analyzed included sex, race, age at diagnosis, year of diagnosis, pathological type, number of tumor foci, tumor extension, T-stage, N-stage, surgical procedure and radiation. Univariate and multivariate analyses were conducted to evaluate factors influencing distant metastasis of pediatric thyroid cancer. Results: In the univariate analysis, factors influencing distant metastasis of thyroid cancer were age at diagnosis (P 0.05). Furthermore, according to chi-squared test, younger pediatric thyroid cancer patients with higher T- and N-stages are more likely to have distant metastasis. Conclusion: Age at diagnosis, T-stage and N-stage influence distant metastasis of thyroid cancer patients aged 2 to 18 years; accordingly, more radical treatments may need to be used for patients with those risk elements

QSAR Studies on Andrographolide Derivatives as α-Glucosidase Inhibitors

Andrographolide derivatives were shown to inhibit α-glucosidase. To investigate the relationship between activities and structures of andrographolide derivatives, a training set was chosen from 25 andrographolide derivatives by the principal component analysis (PCA) method, and a quantitative structure-activity relationship (QSAR) was established by 2D and 3D QSAR methods. The cross-validation r2 (0.731) and standard error (0.225) illustrated that the 2D-QSAR model was able to identify the important molecular fragments and the cross-validation r2 (0.794) and standard error (0.127) demonstrated that the 3D-QSAR model was capable of exploring the spatial distribution of important fragments. The obtained results suggested that proposed combination of 2D and 3D QSAR models could be useful in predicting the α-glucosidase inhibiting activity of andrographolide derivatives

Condition Monitoring of Wind Turbine Based on Copula Function and Autoregressive Neural Network

The traditional wind turbine fault monitoring is often based on a single monitoring signal without considering the overall correlation between signals. A global condition monitoring method based on Copula function and autoregressive neural network is proposed for this problem. Firstly, the Copula function was used to construct the binary joint probability density function of the power and wind speed in the fault-free state of the wind turbine. The function was used as the data fusion model to output the fusion data, and a fault-free condition monitoring model based on the auto-regressive neural network in the faultless state was established. The monitoring model makes a single-step prediction of wind speed and power, and statistical analysis of the residual values of the prediction determines whether the value is abnormal, and then establishes a fault warning mechanism. The experimental results show that this method can provide early warning and effectively realize the monitoring of wind turbine condition

Condition Monitoring of Wind Turbine Based on Copula Function and Autoregressive Neural Network

The traditional wind turbine fault monitoring is often based on a single monitoring signal without considering the overall correlation between signals. A global condition monitoring method based on Copula function and autoregressive neural network is proposed for this problem. Firstly, the Copula function was used to construct the binary joint probability density function of the power and wind speed in the fault-free state of the wind turbine. The function was used as the data fusion model to output the fusion data, and a fault-free condition monitoring model based on the auto-regressive neural network in the faultless state was established. The monitoring model makes a single-step prediction of wind speed and power, and statistical analysis of the residual values of the prediction determines whether the value is abnormal, and then establishes a fault warning mechanism. The experimental results show that this method can provide early warning and effectively realize the monitoring of wind turbine condition

Spectrophotometric flow injection determination of dissolved titanium in seawater exploiting in-line nitrilotriacetic acid resin preconcentration and a long path length liquid waveguide capillary cell

A sensitive spectrophotometric method for the determination of dissolved titanium (Ti) in seawater is developed. It involves in-line preconcentration and a long path length liquid waveguide capillary cell (LWCC). Nitrilotriacetic acid (NTA) resin is used to preconcentrate Ti from ∼25 mL seawater sample at pH 1.7, and elution is accomplished with 0.8 mol L−1 hydrochloride acid. The eluted Ti solution is buffered to pH 6.0 with 1.0 mol L−1 ammonium acetate and mixed with 1.5 mmol L−1 Tiron solution. The mixture is then injected into LWCC and measured by spectrophotometry at 420 nm. Before the preconcentration step, the sample is treated with 7 mmol L−1 ascorbic acid to reduce Fe(III) to Fe(II), in order to eliminate the Fe interference. The method is not interfered by Fe(III) and Cu(II) present in seawater samples at concentrations 50-fold higher in relation to Ti, and by Cd(II), Pb(II), Cr(VI), Mn(II), Al(III), Zn(II), and Ni(II) at concentrations 100-fold higher in relation to Ti. It is time efficient (7.5 minutes per sample), sensitive (0.10 nmol L−1 detection limit), precise (1.40% measurement RSD at 1.00 nmol L−1 Ti) and is characterized by a linear range of 0.50–5.00 nmol L−1 Ti. The method was applied to analysis of natural water samples collected from the Jiulongjiang Estuary, Fujian, China. [Display omitted] •Sensitive spectrophotometric detection of dissolved Ti in seawater is developed.•NTA resin is used to preconcentrate dissolved Ti in seawater at pH 1.7.•Fe(III) is reduced to Fe(II) before resin to avoid Fe retention and interference.•In-line preconcentration and detection is realized with flow analysis and LWCC.•Estuarine dissolved Ti is analyzed and reported

Application of Minnan Folk Light and Shadow Animation in Built Environment in Object Detection Algorithm

To resolve the problems of deep convolutional neural network models with many parameters and high memory resource consumption, a lightweight network-based algorithm for building detection of Minnan folk light synthetic aperture radar (SAR) images is proposed. Firstly, based on the rotating target detection algorithm R-centernet, the Ghost ResNet network is constructed to reduce the number of model parameters by replacing the traditional convolution in the backbone network with Ghost convolution. Secondly, a channel attention module integrating width and height information is proposed to enhance the network’s ability to accurately locate salient regions in folk light images. Content-aware reassembly of features (CARAFE) up-sampling is used to replace the deconvolution module in the network to fully incorporate feature map information during up-sampling to improve target detection. Finally, the constructed dataset of rotated and annotated light and shadow SAR images is trained and tested using the improved R-centernet algorithm. The experimental results show that the improved algorithm improves the accuracy by 3.8%, the recall by 1.2% and the detection speed by 12 frames/second compared with the original R-centernet algorithm

Reconciling ASPP-p53 binding mode discrepancies through an ensemble binding framework that bridges crystallography and NMR data.

ASPP2 and iASPP bind to p53 through their conserved ANK-SH3 domains to respectively promote and inhibit p53-dependent cell apoptosis. While crystallography has indicated that these two proteins employ distinct surfaces of their ANK-SH3 domains to bind to p53, solution NMR data has suggested similar surfaces. In this study, we employed multi-scale molecular dynamics (MD) simulations combined with free energy calculations to reconcile the discrepancy in the binding modes. We demonstrated that the binding mode based solely on a single crystal structure does not enable iASPP's RT loop to engage with p53's C-terminal linker-a verified interaction. Instead, an ensemble of simulated iASPP-p53 complexes facilitates this interaction. We showed that the ensemble-average inter-protein contacting residues and NMR-detected interfacial residues qualitatively overlap on ASPP proteins, and the ensemble-average binding free energies better match experimental KD values compared to single crystallgarphy-determined binding mode. For iASPP, the sampled ensemble complexes can be grouped into two classes, resembling the binding modes determined by crystallography and solution NMR. We thus propose that crystal packing shifts the equilibrium of binding modes towards the crystallography-determined one. Lastly, we showed that the ensemble binding complexes are sensitive to p53's intrinsically disordered regions (IDRs), attesting to experimental observations that these IDRs contribute to biological functions. Our results provide a dynamic and ensemble perspective for scrutinizing these important cancer-related protein-protein interactions (PPIs)

A Model of Interaction between Nicotinamide Adenine Dinucleotide Phosphate (NADPH) Oxidase and Apocynin Analogues by Docking Method

Abstract: Some apocynin analogues have exhibited outstanding inhibition to NADPH oxidase. In this study, the key interactions between apocynin analogues and NADPH oxidase were analyzed by the docking method. The potential active site was first identified by the SiteID program combining with the key residue CYS378. Afterwards, the compounds in the training set were docked into NADPH oxidase (1K4U) under specific docking constraints to discuss the key interactions between ligands and the receptor. These key interactions were then validated by the consistence between the docking result and the experimental result of the test set. The result reveals that the Pi interaction between apocynin analogues and NADPH oxidase has a direct contribution to inhibition activities, except for H-bond formation and docking score. The key interactions might be valuable to discover and screen apocynin analogues as potent inhibitors of NADPH oxidase