MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review.

With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving. The process of mmWave radar and vision fusion is then divided into three parts: sensor deployment, sensor calibration, and sensor fusion, which are reviewed comprehensively. Specifically, we classify the fusion methods into data level, decision level, and feature level fusion methods. In addition, we introduce three-dimensional(3D) object detection, the fusion of lidar and vision in autonomous driving and multimodal information fusion, which are promising for the future. Finally, we summarize this article.

A systems approach to investigate GPCR-mediated Ras signaling network in chemoattractant sensing.

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.

SBML Level 3: an extensible format for the exchange and reuse of biological models.

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.

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2.

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.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2.

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/.

Correction to: Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE).

[This corrects the article DOI: 10.4056/sigs.5279417.].

SBML Level 3 package: Multistate, Multicomponent and Multicompartment Species, Version 1, Release 1.

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.

NetworkViewer: visualizing biochemical reaction networks with embedded rendering of molecular interaction rules.

BACKGROUND: Network representations of cell-biological signaling processes frequently contain large numbers of interacting molecular and multi-molecular components that can exist in, and switch between, multiple biochemical and/or structural states. In addition, the interaction categories (associations, dissociations and transformations) in such networks cannot satisfactorily be mapped onto simple arrows connecting pairs of components since their specifications involve information such as reaction rates and conditions with regard to the states of the interacting components. This leads to the challenge of having to reconcile competing objectives: providing a high-level overview without omitting relevant information, and showing interaction specifics while not overwhelming users with too much detail displayed simultaneously. This problem is typically addressed by splitting the information required to understand a reaction network model into several categories that are rendered separately through combinations of visualizations and/or textual and tabular elements, requiring modelers to consult several sources to obtain comprehensive insights into the underlying assumptions of the model. RESULTS: We report the development of an application, the Simmune NetworkViewer, that visualizes biochemical reaction networks using iconographic representations of protein interactions and the conditions under which the interactions take place using the same symbols that were used to specify the underlying model with the Simmune Modeler. This approach not only provides a coherent model representation but, moreover, following the principle of "overview first, zoom and filter, then details-on-demand," can generate an overview visualization of the global network and, upon user request, presents more detailed views of local sub-networks and the underlying reaction rules for selected interactions. This visual integration of information would be difficult to achieve with static network representations or approaches that use scripted model specifications without offering simple but detailed symbolic representations of molecular interactions, their conditions and consequences in terms of biochemical modifications. CONCLUSIONS: The Simmune NetworkViewer provides concise, yet comprehensive visualizations of reaction networks created in the Simmune framework. In the near future, by adopting the upcoming SBML standard for encoding multi-component, multi-state molecular complexes and their interactions as input, the NetworkViewer will, moreover, be able to offer such visualization for any rule-based model that can be exported to that standard.

The Simmune Modeler visual interface for creating signaling networks based on bi-molecular interactions.

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!).

Computational modeling of cellular signaling processes embedded into dynamic spatial contexts.

Cellular signaling processes depend on spatiotemporal distributions of molecular components. Multicolor, high-resolution microscopy permits detailed assessment of such distributions, providing input for fine-grained computational models that explore mechanisms governing dynamic assembly of multimolecular complexes and their role in shaping cellular behavior. However, it is challenging to incorporate into such models both complex molecular reaction cascades and the spatial localization of signaling components in dynamic cellular morphologies. Here we introduce an approach to address these challenges by automatically generating computational representations of complex reaction networks based on simple bimolecular interaction rules embedded into detailed, adaptive models of cellular morphology. Using examples of receptor-mediated cellular adhesion and signal-induced localized mitogen-activated protein kinase (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 new version of the Simmune toolset, are accessible through intuitive graphical interfaces and programming libraries.

Sequence context analysis in the mouse genome: single nucleotide polymorphisms and CpG island sequences.

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.

Sequence context analysis of 8.2 million single nucleotide polymorphisms in the human genome.

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.

SNPNB: analyzing neighboring-nucleotide biases on single nucleotide polymorphisms (SNPs).

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/

The influence of neighboring-nucleotide composition on single nucleotide polymorphisms (SNPs) in the mouse genome and its comparison with human SNPs.

We analyzed the neighboring-nucleotide composition of 433,192 biallelic substitutions, representing the largest public collection of SNPs across the mouse genome. Large neighboring-nucleotide biases relative to the genome- or chromosome-specific average were observed at the immediate adjacent sites and small biases extended farther from the substitution site. For all substitutions, the biases for A, C, G, and T were 0.21, 2.63, 0.71, and -3.55%, respectively, on the immediate adjacent 5' site and -3.67, 0.75, 2.69, and 0.23%, respectively, on the immediate adjacent 3' side. Further examination of the six categories of substitution revealed that the neighboring-nucleotide patterns for transitions were strongly influenced by the hypermutability of dinucleotide CpG and the neighboring effects on transversions were complex. Probability of a transversion increased with increasing A + T content of the two immediate adjacent sites, which was similarly observed in the human and Arabidopsis genomes. Overall, the bias patterns for the neighboring nucleotides in the mouse and human genomes were essentially the same; however, the extent of the biases was notably less in mice. Our results provide the first comprehensive view of the neighboring-nucleotide effects in the mouse genome and are important for understanding the mutational mechanisms and sequence evolution in the mammalian genomes.