Solid Edge V19 Licence File 12

6. THIRD PARTY SOFTWARE. Certain third party software provided with or within the Software may only be used (a) upon securing a license directly from the owner of the software or (b) in combination with hardware components purchased from such third party and (c) subject to further license limitations by the software owner. A listing of any such third party limitations is in one or more text files accompanying the Software. You acknowledge Intel is not providing You with a license to such third party software and further that it is Your responsibility to obtain appropriate licenses from such third parties directly.

ObjectAnything recognizable by the tools. 3D objects include vertices, edges, faces, surfaces, solids, layouts, planes, axes, and origins. 2D objects include points and lines. IDs for edges, faces, and bodies are now stored within the .scdoc file. Object IDs are preserved when other files are opened or inserted into, and the IDs can also be exported. For example, if you export a design to an analysis company, and they tag geometry with load positions, boundary conditions, and so on, then when you re-import that design, make changes, and re-export to the analysis company, they will not need to recreate their tags on the new design.

For CATIAModeling engine by Dassault Systèmes. You can import and export CATIA files., ParasolidParasolid geometric modeling kernel. You can open and insert parts and assemblies and export parts and assemblies., STL, and STEP files, you can select which version or protocol to save as. You can also set your default export options by clicking Options.

You can save documents that only contain sketch curves to ACISModeling engine by Spatial Corporation. You can import and export ACIS files (.sat and .sab). binary (.sab), ACIS text (.sat), Parasolid, CATIA, IGES, STEP, and VDA formats. You can import and export free points for Rhino, PDF, ACIS, IGES, JT Open, Parasolid, STEP and VDA formats.

When you import ACIS files, the instance name "part n (body m)" is now imported, but only if the body name is different from the part name. The component and body names are separated by a character which you can define in the options for ACIS files. For example, the default character is a period, so the imported name would be component.body. This way, if there were one body named wheel in one component, the name of the imported component in SC would be wheel. An instance is a copy of a body (a copied or pattered solid).

LineA straight line, arc, or spline drawn in Sketch mode or on a layout plane. Lines have length but no area. When you pull a sketch into 3D with the PullTool used to distort or deform geometry. Use the Pull tool to offset, extrude, revolve, sweep, draft, and blend faces; or to round, chamfer, or extrude edges. When converting a sketch to 3D, pulling a line creates a surface and pulling a surface creates a solid. tool, lines become edges. weights can be exported to AutoCAD (DXF or DWG). Hatch lines on drawing sheets are exported as stand-alone lines.

When exporting CATIA V5 files, you can deselect the Simplify SplineA continuously curved line, without sharp boundaries (that is, without vertices). Create a spline by defining a set of points using the Spline tool. A spline becomes an edge when you pull it with the Pull tool. Surface Data option. When importing or exporting CATIA files, the XYZ locations of point objects scale correctly.

ComponentObject in a design, including the top-level design component. Each component consists of any number of objects, such as solids and surfaces, and can contain sub-components. You can think of a component as a "part." Components can be saved as a separate file. An external component is another design inserted as a component of your design. Making the component internal prevents changes from being made to the external component file. You can also create an external component by saving a component as a separate file. See Lightweight components, Assembly structure

Relevance Networks mainly construct gene regulatory network (GRN) models by calculating the associations between genes. This method considers that genes with similar expression profiles may interact with each other and therefore may have similar functions [106]. If the expression value of gene A is increased and the expression value of gene B is simultaneously increased or decreased, the relationship between the two genes can be detected and modeled. The regulatory relationship can also be inferred by the transcriptional dependence between them. The main idea of the correlation detection method is that for a predetermined threshold if the association between genes is higher than the threshold, the genes will be connected by edges in the network. Two genes are more related if they have the same or similar regulatory mechanisms, especially for target genes of the same transcription factor or genes on the same biological pathway. The relevance between genes can be inferred with the following metrics.

Liang et al. [162] first proposed to predict possible GRN structures from gene expression data using Boolean networks and developed a Boolean network-based software Reverse Engineering Algorithm (REVEAL) by considering the information entropy between nodes to help build the network structure. Kim et al. [163] proposed to utilize chi-squared tests to eliminate uncorrelated edges between nodes to accelerate the search for the optimal network structure. Due to the stochastic nature of biological systems and the noise contained in gene expression data, Boolean networks as deterministic models are not able to capture network regulatory relationships accurately. To solve this problem, a combination of the Boolean network and Markov chain was developed into the Probabilistic Boolean Network (PBN) model [164], which is a more flexible topology that adds stochasticity to the original network and can better handle the uncertainties among genes in the probabilistic framework. Boolean networks can be combined with MI to infer the structural and dynamic relationships between genes for time-series data [165]. The Single Cell Network Synthesis toolkit (SCNS) [166] is a computational tool for reconstructing and analyzing executable models from single-cell gene expression data. SCNS constructs a state transition graph of binary expression profiles using single-cell qPCR or RNA sequencing data acquired over the entire time course. An asynchronous Boolean network model is built by searching for rules that drive the transition from early to late cell states and thus reconstructing Boolean logical regulatory rules.

By merging SEER-Medicare data, we created a unique technique to find prognostic and chemopredictive biomarkers with the potential to be used in large patient populations to fill this gap [221]. The SEER database is a compilation of registration information from specific geographic areas, which account for around 26% of the U.S. population [222]. Without additional natural language processing, the linked SEER-Medicare data are adequately annotated and prepared for computational analysis. A previous study identified chemopredictive genes by correlating mRNA expression profiles in solid tumors in the advanced cancer stage of a Serial Analysis of Gene Expression (SAGE) database with patient survival in SEER data [223]. In our previous study, a novel tumor progression indicator, combining AJCC cancer staging [224] T, N, and M factors with tumor grade was used to correlate miRNA expression in a lung squamous cell carcinoma (LUSC) patient cohort with SEER-medicare LUSC patient outcomes receiving different treatments. The identified chemopredictive miRNAs were then validated with extensive pubic data and our collected patient cohorts. Our study revealed miRNA-mediated transcriptional networks in NSCLC proliferation and progression using CRISPR-Cas9/RNAi screening data [221]. Our findings show that, in the absence of novel cohorts with tens of thousands of patients who have matched clinical outcomes and genome-scale transcriptomic profiles, extrapolation of miRNA expression from smaller cohorts to larger population-based data can serve as an additional confirmatory tool based on similarities in tumor progression. This method, in conjunction with stringent external validation, can discover prognostic and predictive biomarkers with concordant expression patterns in tumor development in sizable patient populations. 2b1af7f3a8