Information integration and management through the design and construction phases of a project life cycle

Information management is a key aspect of successful construction projects. Having inaccurate measurements and conflicting data can lead to costly mistakes, and vague quantities can ruin estimates and schedules. Building information modeling (BIM) augments a 3D model with a wide variety of information, which reduces many sources of error and can detect conflicts before they occur. Because new technology is often more complex, it can be difficult to effectively integrate it with existing business practices. In this paper, we will answer two questions: How can BIM add value to construction projects? and What lessons can be learned from other companies that use BIM or other similar technology? Previous research focused on the technology as if it were simply a tool, observing problems that occurred while integrating new technology into existing practices. Our research instead looks at the flow of information through a company and its network, seeing all the actors as part of an ecosystem. Building upon this idea, we proposed the metaphor of an information supply chain to illustrate how BIM can add value to a construction project. This paper then concludes with two case studies. The first case study illustrates a failure in the flow of information that could have prevented by using BIM. The second case study profiles a leading design firm that has used BIM products for many years and shows the real benefits of using this program.

POPLAR GENE EXPRESSION DATA ANALYSIS PIPELINES

Analyzing large-scale gene expression data is a labor-intensive and time-consuming process. To make data analysis easier, we developed a set of pipelines for rapid processing and analysis poplar gene expression data for knowledge discovery. Of all pipelines developed, differentially expressed genes (DEGs) pipeline is the one designed to identify biologically important genes that are differentially expressed in one of multiple time points for conditions. Pathway analysis pipeline was designed to identify the differentially expression metabolic pathways. Protein domain enrichment pipeline can identify the enriched protein domains present in the DEGs. Finally, Gene Ontology (GO) enrichment analysis pipeline was developed to identify the enriched GO terms in the DEGs. Our pipeline tools can analyze both microarray gene data and high-throughput gene data. These two types of data are obtained by two different technologies. A microarray technology is to measure gene expression levels via microarray chips, a collection of microscopic DNA spots attached to a solid (glass) surface, whereas high throughput sequencing, also called as the next-generation sequencing, is a new technology to measure gene expression levels by directly sequencing mRNAs, and obtaining each mRNA’s copy numbers in cells or tissues. We also developed a web portal (http://sys.bio.mtu.edu/) to make all pipelines available to public to facilitate users to analyze their gene expression data. In addition to the analyses mentioned above, it can also perform GO hierarchy analysis, i.e. construct GO trees using a list of GO terms as an input.