A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides

Preparation and Interfacial Properties of Hydroxyl-Containing Polyimide Fibers

Developing polyimide (PI) fibers with excellent interfacial adhesion and high mechanical properties for the PI fiber-reinforced polymer matrix composites (PFRPs) industry has been challenging. In this work, 4,4′-diamino-(1,1′-biphenyl)-3,3′-diol (HAB) diamine was introduced into the rigid molecular chains, and the high-performance PI fibers, presenting an interfacial shear strength (IFSS) value of 46.33 MPa, tensile strength of 2.62 GPa, and modulus of 100.15 GPa, were successfully manufactured when the content of HAB in mixed diamines was 30 mol %. Fourier transform infrared (FTIR) spectroscopy identified the presence of intermolecular H-bonding interactions, and 2D small-angle X-ray scattering indicated that the introduction of HAB moiety contributed to reducing the radii of microvoids in the fibers, which were considered to be the key factors leading to a significant enhancement in the mechanical properties of the fibers. X-ray photoelectron spectroscopy (XPS) and the static contact angle intuitively illustrated that the synthetic fiber surface contained active hydroxyl groups. The IFSS value of PI fiber/epoxy resin composites (PI/EPs) was 56.47 MPa when the content of HAB reached 70 mol %. Failure morphologies confirmed that the interfacial adhesion of PI/EPs was enhanced owing to the surface activity of PI fibers. Consequently, this study provides an effective strategy to the long-standing problems of high mechanical performances and poor surface activity for traditional PI fibers used in the PFRPs industry

Nanoparticles-Enabled Surface-Enhanced Imaging Ellipsometry for Amplified Biosensing

The main issues of imaging ellipsometry-based biosensing for small molecules are the low sensitivity and narrow detection range due to the low molecular weight of small molecules that results in a negligible signal. To meet this challenge, we theoretically investigated the deciding factors of the ellipsometry signal and further applied the theory to guide the design of ellipsometry-based biosensor using metal nanoparticles that have a high dielectric constant. Significant signal amplification effects can be achieved by using nanoparticle labels including magnetic nanoparticles and gold nano-particles. Guided by the theory, we have developed a sensitive surface-enhanced imaging ellipsometry (SEIE)-biosensor for detecting chloramphenicol in real milk sample with high sensitivity (with a limit of detection of 6 pg/mL) and broaden detection range. This nanoparticles-enabled SEIE not only greatly improves the sensitivity of conventional imaging ellipsometry-based biosensors but also retains the advantages of conventional methods in terms of automated and convenient operation, providing an effective strategy for detection of trace small molecules in complex samples that holds great promise in scientific research, clinical diagnosis, and food safety

Expression of β-catenin is upregulated during odontoblastic differentiation of DPCs.

<p>(A) Alizarin red staining of DPCs on day 7 and 14 after odontoblastic induction. (B) ALP activity of DPCs on day 0, 7 and 14 after odontoblastic induction. ALP activity was calculated as moles of p-nitrophenol per mg protein. (C) mRNA levels of DSPP, DMP-1, BSP and OCN on day 0, 7, and 14 after odontoblastic induction. (D) Protein expression of DSP and BSP on day 0, 7, and 14 after odontoblastic induction. (E) mRNA level of β-catenin on day 0, 7 and 14 after odontoblastic induction. (F) Protein expression of β-catenin on day 0, 7 and 14 after odontoblastic induction. GAPDH served as an internal control. *<i>P</i><0.05, compared with the previously adjacent cell group; †<i>P</i><0.05, compared with cell group at day 0.</p

β-catenin promotes odontoblastic differentiation through activation of Runx2.

<p>mRNA level (A) and protein expression (B) of Runx2 in DPCs transfected with shRNA-NT or shRNA-β-cat. Effects of the activation of β-catenin by LiCl treatment on the expression of Runx2 at mRNA level (C) and protein expression (D). (E) Changes in binding of β-catenin to the chip region of promoter of Runx2 on day 0 and day 14 during odontoblastic differentiation induction. (F) Effects of the β-catenin knockdown and the accumulation of β-catenin by LiCl treatment on the binding of β-catenin to the promoter of Runx2. *<i>P</i><0.05, compared with the previously adjacent cell group.</p

Knockdown of β-catenin inhibits odontoblastic differentiation of DPCs.

<p>(A) DPCs transfected with shRNA-NT or shRNA-β-cat on day 14 after transfection (EGFP positive). (B, C, D, E, F, G) Effects of shRNA-β-cat on the mRNA expression of β-catenin, DSPP, DMP1, ALP, BSP and OCN after odontoblastic induction, respectively. GAPDH served as an internal control. (H) Effect of shRNA-β-cat on the protein expressions of β-catenin, DSP and BSP on day 0, 7 and 14 after odontoblastic induction. (I) Alizarin red staining of DPCs on day 14 after odontoblastic induction with shRNA infection or LiCl treatment. *<i>P</i><0.05, compared with previously adjacent cell group.</p

β-catenin accumulation induced by LiCl treatment promotes odontoblastic differentiation of DPCs.

<p>(A) Effects of LiCl treatment on protein expression of β-catenin, DSP and BSP. GAPDH served as an internal control. (B, C, D, E, F) mRNA levels of DSPP, DMP1, ALP, BSP and OCN after odontoblastic induction with LiCl treatment, respectively. *<i>P</i><0.05, compared with previously adjacent cell group.</p

Gclust: A Parallel Clustering Tool for Microbial Genomic Data

The accelerating growth of the public microbial genomic data imposes substantial burden on the research community that uses such resources. Building databases for non-redundant reference sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algorithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demonstrate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust. Keywords: Microbial genome clustering, Parallelization, Sparse suffix array, Maximal exact match, Segment extensio

Nanoparticles-Enabled Surface-Enhanced Imaging Ellipsometry for Amplified Biosensing

The main issues of imaging ellipsometry-based biosensing for small molecules are the low sensitivity and narrow detection range due to the low molecular weight of small molecules that results in a negligible signal. To meet this challenge, we theoretically investigated the deciding factors of the ellipsometry signal and further applied the theory to guide the design of ellipsometry-based biosensor using metal nanoparticles that have a high dielectric constant. Significant signal amplification effects can be achieved by using nanoparticle labels including magnetic nanoparticles and gold nano-particles. Guided by the theory, we have developed a sensitive surface-enhanced imaging ellipsometry (SEIE)-biosensor for detecting chloramphenicol in real milk sample with high sensitivity (with a limit of detection of 6 pg/mL) and broaden detection range. This nanoparticles-enabled SEIE not only greatly improves the sensitivity of conventional imaging ellipsometry-based biosensors but also retains the advantages of conventional methods in terms of automated and convenient operation, providing an effective strategy for detection of trace small molecules in complex samples that holds great promise in scientific research, clinical diagnosis, and food safety.</p