Ground-penetrating radar (GPR) with different center frequencies can detect defects at different depths with a range of resolutions enabling it to be used for subsurface defect inspection. However, the existing deep learning methods cannot accurately invert the permittivity from GPR data of different frequencies, due to the limited number of labeled GPR images for every center frequency. To tackle this challenge, a semi-supervised deep neural network-based cross-frequency GPR data inversion method was proposed, which enables the generalized model to be trained on the GPR data with one frequency (source domain) for migration to other frequencies (target domain). The method was trained in a semi-supervised manner using a small number of paired GPR data with permittivity labels and a large amount of unlabeled GPR data without corresponding permittivity maps. An adversarial learning mechanism together with a novel random perturbation strategy was designed to improve the global inversion performance for a large-scale structure and avoid a discontinuity in the reconstructed shapes. Furthermore, a mean teacher architecture is introduced to improve the inversion accuracy of detailed information from the unlabeled GPR data under different perturbation conditions. The ablation and comparative experiments results indicated that the proposed method outperforms other methods and can be effectively generalized to GPR B-Scan data with different frequencies and signal-to-noise ratios. In addition, sandbox model testing was conducted and the results indicate that this method can transfer the knowledge from the synthetic data domain to the real data domain with satisfactory results.

Ground penetrating radar (GPR) enables infrastructure inspection using consecutive and long survey lines. However, the existing GPR data processing methods may lead to distortions or dislocations in the reconstructed shapes of the detected objects or even inconsistencies of the inverted dielectric values when performing inversion or object identification directly using GPR data obtained from the consecutive and long survey lines. To overcome these issues, this study proposed a novel deep neural network (DNN) architecture named GPRI2Net to simultaneously reconstruct the permittivity maps and categorize the object class labels from the GPR data of consecutive and long survey lines. GPRI2Net combined a convolutional neural network (CNN) based on DenseUnet and a recurrent neural network (RNN) based on bidirectional convolutional long short-term memory (Bi-ConvLSTM) to exploit the contextual information in and between the B-Scan segments extracted from the GPR data of a consecutive and long survey line. In addition, GPRI2Net performed inversion and object identification simultaneously using one network, which highly shared features between the two tasks and greatly reduced the computational complexity. Validation experiments were performed at two levels: first, using synthetic data based on the tunnel liner defects model and then using a sandbox model test in a realistic scenario. The results demonstrated that GPRI2Net can reconstruct consecutive permittivity maps and categorize the object class labels from GPR data with different dominant frequencies and survey line lengths and achieved a superior performance using the synthetic data compared to several other methods. Moreover, GPRI2Net also achieved satisfactory results using real-world GPR data.

MOTIVATION: Biochemical modeling efforts now frequently take advantage of the possibility to automatically create reaction networks based on the specification of pairwise molecular interactions. Even though a variety of tools exist to visualize the resulting networks, defining the rules for the molecular interactions typically requires writing scripts, which impacts the non-specialist accessibility of those approaches. We introduce the Simmune Modeler that allows users to specify molecular complexes and their interactions as well as the reaction-induced modifications of the molecules through a flexible visual interface. It can take into account the positions of the components of trans-membrane complexes relative to the embedding membranes as well as symmetry aspects affecting the reactions of multimeric molecular structures. Models created with this tool can be simulated using the Simmune Simulator or be exported as SBML code or as files describing the reaction networks as systems of ODEs for import into Matlab. AVAILABILITY: The Simmune Modeler and the associated simulators as well as extensive additional documentation and tutorials are freely available for Linux, Mac and Windows: http://go.usa.gov/QeH (Note shortened case-sensitive URL!).

UNLABELLED: SNPNB is a user-friendly and platform-independent application for analyzing Single Nucleotide Polymorphism NeighBoring sequence context and nucleotide bias patterns, and subsequently evaluating the effective SNP size for the bias patterns observed from the whole data. It was implemented by Java and Perl. SNPNB can efficiently handle genome-wide or chromosome-wide SNP data analysis in a PC or a workstation. It provides visualizations of the bias patterns for SNPs or each type of SNPs. AVAILABILITY: SNPNB and its full description are freely available at http://bioinfo.vipbg.vcu.edu/SNPNB/