Knowledge discovery from post-project reviews

This article was published in the journal, Construction Management and Economics [© Taylor & Francis (Routledge)] and the definitive version is available at: http://dx.doi.org/10.1080/01446193.2011.588953Many construction companies conduct reviews on project completion to enhance learning and to fulfil quality management procedures. Often these reports are filed away never to be seen again. This means that potentially important knowledge that may assist other project teams is not exploited. In order to ascertain whether useful knowledge can be gleaned from such reports, Knowledge Discovery from Text (KDT) and text mining (TM) are applied. Text mining avoids the need for a manual search through a vast number of reports, potentially of different formats and foci, to seek trends that may be useful for current and future projects. Pilot tests were used to analyse 48 post-project review reports. The reports were first reviewed manually to identify key themes. They were then analysed using text mining software to investigate whether text mining could identify trends and uncover useful knowledge from the reports. Pilot tests succeeded in finding common occurrences across different projects that were previously unknown. Text mining could provide a potential solution and would aid project teams to learn from previous projects. However, a lot of work is currently required before the text mining tests are conducted and the results need to be examined carefully by those with domain knowledge to validate the results obtained

Multigene RNA Vector Based on Coronavirus Transcription

Coronavirus genomes are the largest known autonomously replicating RNAs with a size of ca. 30 kb. They are of positive polarity and are translated to produce the viral proteins needed for the assembly of an active replicase-transcriptase complex. In addition to replicating the genomic RNA, a key feature of this complex is a unique transcription process that results in the synthesis of a nested set of six to eight subgenomic mRNAs. These subgenomic mRNAs are produced in constant but nonequimolar amounts and, in general, each is translated to produce a single protein. To take advantage of these features, we have developed a multigene expression vector based on human coronavirus 229E. We have constructed a prototype RNA vector containing the 5′ and 3′ ends of the human coronavirus genome, the entire human coronavirus replicase gene, and three reporter genes (i.e., the chloramphenicol acetyltransferase [CAT] gene, the firefly luciferase [LUC] gene, and the green fluorescent protein [GFP] gene). Each reporter gene is located downstream of a human coronavirus transcription-associated sequence, which is required for the synthesis of individual subgenomic mRNAs. The transfection of vector RNA and human coronavirus nucleocapsid protein mRNA into BHK-21 cells resulted in the expression of the CAT, LUC, and GFP reporter proteins. Sequence analysis confirmed the synthesis of coronavirus-specific mRNAs encoding CAT, LUC, and GFP. In addition, we have shown that human coronavirus-based vector RNA can be packaged into virus-like particles that, in turn, can be used to transduce immature and mature human dendritic cells. In summary, we describe a new class of eukaryotic, multigene expression vectors that are based on the human coronavirus 229E and have the ability to transduce human dendritic cells