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SUMMARY During tunnel construction, accurately ascertaining the adverse geological condition in front of the tunnel face is essential to ensure construction safety and decrease economic loss. As an accurate wave velocity modelling method, seismic full-waveform inversion (FWI) still faces many difficulties when applied in a tunnel observation environment because of fewer observation data and smaller offset than ground seismic detection. This paper analysed the FWI for tunnel active seismic forward prospecting with a single shot. We adopted the N-order time integral wavefield and normalized integration objective function method to improve the stability and reduce the dependence of FWI on the initial model. Based on two assumptions on the wave velocity distribution in the tunnel, we proposed a 1-D velocity structure correction method to reduce the multiplicity of inversion. Then the improved tunnel FWI method based on these two inversion strategies was applied to a synthetic model with a rectangular anomaly and a lithology interface, verifying the method's effectiveness. Based on typical adverse geological bodies during tunnelling, three additional adverse geological models were built and verified the reliability of the methods in tunnel detection environments. There are still some false anomalies interference in the inversion results of the synthetic models from the inversion results. However, these problems are acceptable in limited tunnel observation space. Then the method was applied in a field example in the Yinsong water supply project in Jilin Province, China, and verified the effect on field data. Finally, the influence of the shot number on the inversion effect and the method's robustness are discussed.

Cross-hole radar is a method for geophysical exploration of geological conditions between two holes that can meet the need for refined detection of fractures. Full waveform inversion and reverse time migration are usually used in geophysical data processing and imaging. Full waveform inversion can invert the high-resolution relative permittivity image of the underground using the full-amplitude and phase information of the radar wave. Reverse time migration can migrate each reflection point in the radar record to its original position, thereby reflecting the real location of the underground interface. In this paper, we combined these two methods to achieve the purpose of refined detection of cross-hole radar for fractures in the tunnel side wall. Through several numerical simulation examples, we found that when there is a certain degree of electrical difference between the fractures and the surrounding rock, full waveform inversion can accurately reflect the distribution of the fractures and the relative permittivity value. Reverse time migration is more prominent in the description of the low-angle fractures interface, which facilitates the accurate division of the fracture. The combination approach of the two methods can accurately reflect the information of fractures in the wall, but the imaging effect for high-angle fractures is not ideal. The engineering data processing results of the cross-hole radar measured on the tunnel side wall of the Yinsong, Jilin, China prove the reliability of the method.

Ground penetrating radar methods have been widely used for geological surveys, nondestructive inspections, advanced detections, and other subsurface structural detection processes. In this study, in order to fulfill the inversion processes for the aforementioned applications and reconstruct the permittivity of geo-structures with different actual sizes, a Deep Neural Network based inversion network was proposed to invert the relative permittivity of geo-structures from GPR B-scans. A network referred to as the Permittivity Inversion Network (PINet) utilizes a time dimension compression operation to address attenuation induced issues. In addition, it employs a global feature encoder in order to extract the global information, as well as automatically learn the spatial alignments between the GPR data and the permittivity images. This study constructed a universal dataset containing models with different actual sizes and target abnormities with different shapes and permittivity, for the purpose of training and validating the PINet. The inversion network was first validated using simulated GPR data. The inversion results demonstrated that the PINet was capable of accurately reconstructing permittivity images from GPR data with different central frequencies. Moreover, the performance of the PINet was verified using real GPR data. The position, rough shape and permittivity of the targets can be reconstructed.

Full waveform inversion (FWI) is an advanced inversion technique for ground penetrating radar (GPR), which could provide quantitative, high-resolution subsurface imaging. FWI has been used widely to process crosshole and on-ground multi-offset GPR data, but its application to on-ground common-offset GPR data is more difficult and being developed. This is mainly because that on-ground common-offset GPR has much less coverage of the subsurface and mainly includes reflective information. The application of conventional FWI to pure reflection data in the absence of a highly accurate starting velocity model is difficult. Here, we demonstrate a means of achieving this successfully by preprocessing the observed data and the residual fields with an integral algorithm, which could produce a more reasonable gradient and therefore lead to better inversion results. Several cases verify the effectiveness of this method. We achieve the simultaneous inversion of relative permittivity and conductivity for on-ground common-offset GPR, and discuss the trade-off between permittivity and conductivity in details. According to the inversion results of test models, it seems that the inversion result of relative permittivity is more credible in most cases.

Tunnel lining internal defect detection is essential for the safe operation of tunnels. This paper presents an automatic scheme based on rotational region deformable convolutional neural network (R2DCNN) and Ground Penetrating Radar (GPR) images for the accurate detection of defects and rebars with arbitrary orientations. The R2DCNN comprises inter-related modules, specifically, deformable convolution, feature fusion, and rotated region detection modules. In this study, synthetic GPR images, including rebars and various structural defects with different permittivities, as well as real GPR images obtained by model experiments, were constructed for the R2DCNN. Improved results were obtained while testing the R2DCNN on GPR images in comparative experiments. The mean average precision of the R2DCNN was enhanced by 8.21% compared to the R2CNN on synthetic GPR images. The R2DCNN showed satisfactory results in on-site experiments, which demonstrated the applicability of the R2DCNN to practical tunnels.

A GPCR-mediated signaling network enables a chemotactic cell to generate adaptative Ras signaling in response to a large range of concentrations of a chemoattractant. To explore potential regulatory mechanisms of GPCR-controlled Ras signaling in chemosensing, we applied a software package, Simmune, to construct detailed spatiotemporal models simulating responses of the cAR1-mediated Ras signaling network. We first determined the dynamics of G-protein activation and Ras signaling in Dictyostelium cells in response to cAMP stimulations using live-cell imaging and then constructed computation models by incorporating potential mechanisms. Using simulations, we validated the dynamics of signaling events and predicted the dynamic profiles of those events in the cAR1-mediated Ras signaling networks with defective Ras inhibitory mechanisms, such as without RasGAP, with RasGAP overexpression, or with RasGAP hyperactivation. We describe a method of using Simmune to construct spatiotemporal models of a signaling network and run computational simulations without writing mathematical equations. This approach will help biologists to develop and analyze computational models that parallel live-cell experiments.

Tunnels in the operation period are faced with different types of diseases, such as lining fracture, lining containing voids and lining leakage. In this paper, the common tunnel lining diseases are classified and ground-penetrating radar (GPR) is adopted to detect the tunnel lining diseases rapidly and nondestructively. The finite-difference time-domain (FDTD) method is used to carry out numerical experiments on GPR detection forward modelling and backward projection imaging. An interpretation criterion of GPR detection was established on typical lining diseases. Based on the dielectric differences between before and after grouting of tunnel lining, GPR is introduced into the evaluation of grouting on leakage in tunnel lining, and the numerical simulation of grouting effect is carried out. It can be concluded that GPR imaging can well-identify the dielectric changes before and after grouting and evaluate the grouting reinforcement effect more intuitively. Finally, this method was applied to a practical case in Kaiyuan tunnel, China. This paper provides an alternative way to detect tunnel lining leakage in operation period and a reference for the evaluation of structural defects reinforcement in underground engineering.

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/.

With China's expanding economy, many tunnels are being designed and constructed. However, tunneling in hazardous geologic terrain, with faults, fractures, water-bearing openings, and other adverse geological conditions, construction safety is seriously endangered. To ensure the safety of tunnel construction, a tunnel geological prospecting method was proposed and applied at the Xiangyun Tunnel in Yunnan Province, China. In the investigation stage, the engineering geological and hydrogeological conditions were analyzed to recognize high-risk sections. In the construction stage, the “tunnel ahead” prospecting scheme was optimized based on the macroscopic geological conditions. Geological mapping of the tunnel, seismic ahead prospecting and transient electromagnetic soundings were employed as well as resistivity models to image potential adverse conditions. Horizontal drilling and tunnel excavation records verified the geophysical predictions and interpretations.

Cross-hole radar is an effective method for geophysical exploration of underground geological conditions. Besides cross-hole radar, some geological information can be obtained by other methods, such as preliminary geological survey, geologic drilling and long-distance geophysical prospecting. This information can work as priori information to help interpret cross-hole radar detection data. Full waveform inversion (FWI) is an advanced inversion method for cross-hole radar, having higher resolution than the ray tomography method. However, traditional FWI for cross-hole radar takes less advantage of the prior information. Therefore, the introduction of the priori information into FWI is worthy of in-depth study to improve inversion quality. Here we use a special penalty function to impose inequality constraint on the objective function, and then calculate a new update step length value different from the traditional FWI. We use this method to introduce the priori information and improve the accuracy and stability of FWI. The reasonable value range of weight coefficients is discussed, which can control the strength of inequality constraint. Several inversion examples are designed to validate the effect of this improved FWI method. The inversion examples for synthetic and field data show that FWI based on inequality constraint for cross-hole radar has better inversion effect than the traditional FWI. To a certain extent, the introduction of inequality constraint can constrain inversion process, improve the inversion stability and provide higher precision inversion results.

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through “wildcards” representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the “type” concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes a medium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications.

Ground penetrating radar is a popular approach to detect defects in tunnel lining. However, the interpretation is usually based on the original image, which is very different from the real shape of the lining defects. Full waveform inversion and reverse time migration are helpful to solve this problem. Full waveform inversion can invert the relative permittivity distribution and reverse time migration can migrate reflection events to their proper locations. Traditional full waveform inversion method is only applicable to cross‐hole ground penetrating radar data or surface multi‐offset ground penetrating radar data. We propose an improved full waveform inversion method which offers satisfactory inversion result for surface common‐offset radar. The forward modelled waveform and the objective function curve show that our new full waveform inversion method is much more accurate than traditional full waveform inversion for common‐offset radar. Traditional reverse time migration has weaker amplitude with increasing depth; we use an energy matrix to improve the imaging effect. Moreover, our reverse time migration is based on the relative permittivity distribution obtained from full waveform inversion, which provides more accurate imaging result. Through several numerical and engineering examples, we discuss the application of both methods in tunnel lining inspection. The results show that for tunnel lining without rebars, the combined methods can give satisfactory imaging results. But the image quality deteriorates rapidly when dealing with rebars.

Adverse geologies are often encountered during tunnel construction, which could seriously endanger the construction. To ensure the safety, it is essential to detect adverse geologies and their water‐bearing situation ahead the tunnel face. Ground‐penetrating radar is a suitable instrument, but the accurate interpretation of its detection results is difficult. In this paper, at first, an improved back projection imaging algorithm is proposed, which can make reflection waves closer to the real geological boundaries with few artificial clutters. And then, forward modelling of ground‐penetrating radar is carried out for typical adverse geologies, such as karst caves, faults, fractured rock masses, fracture network, and water‐bearing body. Their corresponding response features are obtained, accumulating experience for geological interpretation. The above two methods provide the basis for target identification and geological interpretation. In the last part, the application of the above two methods in several engineering cases are given, and their effectiveness is verified.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution., Over the past two decades, scientists from different fields have been developing SBML, a standard format for encoding computational models in biology and medicine. This article summarizes recent progress and gives perspectives on emerging challenges.

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains (components) and the states such as phosphorylation and binding status of the involved components. Fine-grained spatial information such as the locations of the molecular components relative to a membrane (e.g. whether a modeled molecular domain is embedded into the inner leaflet of the cellular plasma membrane) can also be provided. Through wildcards representing component states entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states and multiple compartments. The SBML Level 3 Multi Package (Multistate, Multicomponent and Multicompartment Species Package for SBML Level 3) extends the SBML Level 3 core with the “type” concept in the Species and Compartment classes and therefore reaction rules may contain species that can be patterns and be in multiple locations in reaction rules. Multiple software tools such as Simmune and BioNetGen support the SBML Level 3 Multi package that thus also becomes a medium for exchanging rule-based models.

In recent years, tunnels and underground engineering are developing rapidly, and meanwhile the topography, complex geological conditions making interpretation difficult in case of ground penetrating radar. To solve this problem, we introduce a two-dimensional forward simulation method for ground penetrating radar detection based on finitedifference time-domain method, which is proved to be effective through a numerical example: the position and shape of the adverse geological body can be inferred by analyzing the reflected signal from the finite-difference time-domain simulation results; and the relationship between the relative dielectric constants of the adverse geological body and the surrounding medium can be found by analyzing the phase information of the reflected signal. Keywords—forward simulation; ground penetrating radar; finite-difference time-domain method; perfectly matched layer; ricker wavelet

We analyzed n-mers (n=3-8) in the local environment of 8,249,446 human SNPs and compared their distribution with that in the genome reference sequences. The results revealed that the short sequences, which contained at least one CpG dinucleotide, occurred more frequently in the local SNP sequences than in the genome sequences. To exclude the hypermutability effect of the methylated CpG dinucleotides on the sequence context of SNPs, we examined the distribution patterns for each of the six categories of substitution. We observed the similar pattern (i.e., CpG-containing n-mers vs. non-CpG-containing n-mers) in SNP categories A/G, C/T and C/G but the opposite pattern in category A/T. We next identified 34,928 putative CpG islands in the human genome and located 133,591 SNPs within these islands. In the CpG islands, CpG SNPs were 3.92-fold less prevalent relative to the presence of CpG dinucleotides. Conversely, in the human genome, the frequency of CpG dinucleotides at the polymorphic sites was 6.09 times that in the genome reference sequences. These results support the previous views of mutational suppression at the CpG sites in the CpG islands and hypermutability of the methylated CpG dinucleotides that are prevalent in the non-CpG island sequences in the human genome. Our study represents a comprehensive investigation of the sequence context of SNPs in the human genome and in human CpG islands.

This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013, The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of community standards and formats in computational systems biology and related fields. This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013, and attended by a total of 96 people. This edition pioneered a first day devoted to modeling approaches in biology, which attracted a broad audience of scientists thanks to a panel of renowned speakers. During subsequent days, discussions were held on many subjects including the introduction of new features in the various COMBINE standards, new software tools that use the standards, and outreach efforts. Significant emphasis went into work on extensions of the SBML format, and also into community-building. This year’s edition once again demonstrated that the COMBINE community is thriving, and still manages to help coordinate activities between different standards in computational systems biology. Foundation Pierre-Gilles de Gennes; Institut des Systèmes Complexes and the Groupement de Recherches en Bioinformatique Moléculaire du CNRS; NIH (intramural program of NIAID).

Cellular signaling processes depend on specific spatiotemporal distributions of their molecular components. Multi-color high-resolution microscopy now permits detailed assessment of such distributions, providing the input for fine-grained computational models that explore the mechanisms governing dynamic assembly of multi-molecular complexes and their role in shaping cellular behavior. However, incorporating into such models both complex molecular reaction cascades and the spatial localization of signaling components within dynamic cellular morphologies presents substantial challenges. Here we introduce an approach that addresses these challenges by automatically generating computational representations of complex reaction networks based on simple bi-molecular interaction rules embedded into detailed, adaptive models of cellular morphology. Using examples of receptor-mediated cellular adhesion and signal-induced localized MAPK activation in yeast, we illustrate the capacity of this simulation technique to provide insights into cell biological processes. The modeling algorithms, implemented in a version of the Simmune tool set, are accessible through intuitive graphical interfaces as well as programming libraries.

The PathoSystems Resource Integration Center (PATRIC) is one of eight Bioinformatics Resource Centers (BRCs) funded by the National Institute of Allergy and Infection Diseases (NIAID) to create a data and analysis resource for selected NIAID priority pathogens, specifically proteobacteria of the genera Brucella, Rickettsia and Coxiella, and corona-, calici- and lyssaviruses and viruses associated with hepatitis A and E. The goal of the project is to provide a comprehensive bioinformatics resource for these pathogens, including consistently annotated genome, proteome and metabolic pathway data to facilitate research into counter-measures, including drugs, vaccines and diagnostics. The project's curation strategy has three prongs: 'breadth first' beginning with whole-genome and proteome curation using standardized protocols, a 'targeted' approach addressing the specific needs of researchers and an integrative strategy to leverage high-throughput experimental data (e.g. microarrays, proteomics) and literature. The PATRIC infrastructure consists of a relational database, analytical pipelines and a website which supports browsing, querying, data visualization and the ability to download raw and curated data in standard formats. At present, the site warehouses complete sequences for 17 bacterial and 332 viral genomes. The PATRIC website (https://patric.vbi.vt.edu) will continually grow with the addition of data, analysis and functionality over the course of the project. PHS HHS [HHSN266200400035C]

A genome-wide view of sequence mutability in mice is still limited, although biologists usually assume the same scenario for mice as for humans. In this study, we examined the sequence context in the local environment of 482,528 mouse single nucleotide polymorphisms (SNPs). We found that CpG-containing short sequences, in general, had more representation in the local sequences of SNPs compared to the genome sequences. The extent of this overrepresentation was stronger in mice than in humans, which is inconsistent with previous observations of the weaker neighboring-nucleotide biases on mouse SNPs. To exclude the CpG effect, we compared the distribution patterns of short sequences among the six categories of SNPs. The results revealed an even stronger pattern in the CpG-containing group for C/G substitution compared to for A/G or C/T substitutions. We next performed the first genome-wide sequence context analysis of SNPs in the mouse CpG islands. SNPs occurring at CpG sites were 3.14-fold less prevalent than expected, suggesting the suppression of methylation-dependent deamination in the CpG islands. The extent of this suppression was less in mice than in humans. Finally, compared with humans, the observations of a greater deficit of CpG dinucleotides, a stronger overrepresentation of CpG-containing n-mers surrounding the polymorphic sites, and a higher SNP/genome ratio of CpG dinucleotides in the mouse genome support the “loss of CpG islands” model in the mouse lineage.