Rockburst laboratory tests database - Application of data mining techniques

Rockburst is characterized by a violent explosion of a block causing a sudden rupture in the rock and is quite common in deep tunnels. It is critical to understand the phenomenon of rockburst, focusing on the patterns of occurrence so these events can be avoided and/or managed saving costs and possibly lives. The failure mechanism of rockburst needs to be better understood. Laboratory experiments are undergoing at the Laboratory for Geomechanics and Deep Underground Engineering (SKLGDUE) of Beijing and the system is described. A large number of rockburst tests were performed and their information collected, stored in a database and analyzed. Data Mining (DM) techniques were applied to the database in order to develop predictive models for the rockburst maximum stress (σRB) and rockburst risk index (IRB) that need the results of such tests to be determined. With the developed models it is possible to predict these parameters with high accuracy levels using data from the rock mass and specific project.

Data quality in biofilm high-throughput routine analysis: intralaboratory protocol adaptation and experiment reproducibility

Biofilm research is growing more diverse and dependent on high-throughput technologies and the large-scale production of results aggravates data substantiation. In particular, it is often the case that experimental protocols are adapted to meet the needs of a particular laboratory and no statistical validation of the modified method is provided. This paper discusses the impact of intra-laboratory adaptation and non-rigorous documentation of experimental protocols on biofilm data interchange and validation. The case study is a non-standard, but widely used, workflow for Pseudomonas aeruginosa biofilm development, considering three analysis assays: the crystal violet (CV) assay for biomass quantification, the XTT assay for respiratory activity assessment, and the colony forming units (CFU) assay for determination of cell viability. The ruggedness of the protocol was assessed by introducing small changes in the biofilm growth conditions, which simulate minor protocol adaptations and non-rigorous protocol documentation. Results show that even minor variations in the biofilm growth conditions may affect the results considerably, and that the biofilm analysis assays lack repeatability. Intra-laboratory validation of non-standard protocols is found critical to ensure data quality and enable the comparison of results within and among laboratories.

Estimativa dos parâmetros de compactação de solos usando técnicas de data mining

Dissertação de mestrado integrado em Engenharia Civil

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.