Ontologies, taxonomies, thesauri:learning from texts

The use of ontologies as representations of knowledge is widespread but their construction, until recently, has been entirely manual. We argue in this paper for the use of text corpora and automated natural language processing methods for the construction of ontologies. We delineate the challenges and present criteria for the selection of appropriate methods. We distinguish three ma jor steps in ontology building: associating terms, constructing hierarchies and labelling relations. A number of methods are presented for these purposes but we conclude that the issue of data-sparsity still is a ma jor challenge. We argue for the use of resources external tot he domain specific corpus.

Energy conversion assessment of vacuum, slow and fast pyrolysis processes for low and high ash paper waste sludge

The performance of vacuum, slow and fast pyrolysis processes to transfer energy from the paper waste sludge (PWS) to liquid and solid products was compared. Paper waste sludges with low and high ash content (8.5 and 46.7 wt.%) were converted under optimised conditions for temperature and pellet size to maximise both product yields and energy content. Comparison of the gross energy conversions, as a combination of the bio-oil/tarry phase and char (ECsum), revealed that the fast pyrolysis performance was between 18.5% and 20.1% higher for the low ash PWS, and 18.4% and 36.5% higher for high ash PWS, when compared to the slow and vacuum pyrolysis processes respectively. For both PWSs, this finding was mainly attributed to higher production of condensable organic compounds and lower water yields during FP. The low ash PWS chars, fast pyrolysis bio-oils and vacuum pyrolysis tarry phase products had high calorific values (∼18-23 MJ kg-1) making them promising for energy applications. Considering the low calorific values of the chars from alternative pyrolysis processes (∼4-7 MJ kg-1), the high ash PWS should rather be converted to fast pyrolysis bio-oil to maximise the recovery of usable energy products.

A comparison of high-content screening versus manual analysis to assay the effects of mesenchymal stem cell-conditioned medium on neurite outgrowth in vitro

Bone marrow mesenchymal stem cells (MSCs) promote nerve growth and functional recovery in animal models of spinal cord injury (SCI) to varying levels. The authors have tested high-content screening to examine the effects of MSC-conditioned medium (MSC-CM) on neurite outgrowth from the human neuroblastoma cell line SH-SY5Y and from explants of chick dorsal root ganglia (DRG). These analyses were compared to previously published methods that involved hand-tracing individual neurites. Both methods demonstrated that MSC-CM promoted neurite outgrowth. Each showed the proportion of SH-SY5Y cells with neurites increased by ~200% in MSC-CM within 48 h, and the number of neurites/SH-SY5Y cells was significantly increased in MSC-CM compared with control medium. For high-content screening, the analysis was performed within minutes, testing multiple samples of MSC-CM and in each case measuring >15,000 SH-SY5Y cells. In contrast, the manual measurement of neurite outgrowth from >200 SH-SY5Y cells in a single sample of MSC-CM took at least 1 h. High-content analysis provided additional measures of increased neurite branching in MSC-CM compared with control medium. MSC-CM was also found to stimulate neurite outgrowth in DRG explants using either method. The application of the high-content analysis was less well optimized for measuring neurite outgrowth from DRG explants than from SH-SY5Y cells.