Extracting kinetic information from literature with KineticRE

To better understand the dynamic behavior of metabolic networks in a wide variety of conditions, the field of Systems Biology has increased its interest in the use of kinetic models. The different databases, available these days, do not contain enough data regarding this topic. Given that a significant part of the relevant information for the development of such models is still wide spread in the literature, it becomes essential to develop specific and powerful text mining tools to collect these data. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The approach proposed integrates the development of a novel plug-in over the text mining framework @Note2. In the end, the pipeline developed was validated with a case study on Kluyveromyces lactis, spanning the analysis and results of 20 full text documents.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Osteogenic differentiation of human mesenchymal stem cells in the absence of osteogenic supplements: a surface-roughness gradient study

The use of biomaterials to direct osteogenic differentiation of human mesenchymal stem cells (hMSCs) in the absence of osteogenic supplements is thought to be part of the next generation of orthopedic implants. We previously engineered surface-roughness gradients of average roughness (Ra) varying from the sub-micron to the micrometer range ( 0.5–4.7 lm), and mean distance between peaks (RSm) gradually varying from 214 lm to 33 lm. Here we have screened the ability of such surface-gradients of polycaprolactone to influence the expression of alkaline phosphatase (ALP), collagen type 1 (COL1) and mineralization by hMSCs cultured in dexamethasone (Dex)-deprived osteogenic induction medium (OIM) and in basal growth medium (BGM). Ra 1.53 lm/RSm 79 lm in Dex-deprived OI medium, and Ra 0.93 lm/RSm 135 lm in BGM consistently showed higher effectiveness at supporting the expression of the osteogenic markers ALP, COL1 and mineralization, compared to the tissue culture polystyrene (TCP) control in complete OIM. The superior effectiveness of specific surface-roughness revealed that this strategy may be used as a compelling alternative to soluble osteogenic inducers in orthopedic applications featuring the clinically relevant biodegradable polymer polycaprolactone.