Potential of Mauritius Hemp (Furcraea gigantea Vent.) for the Remediation of Chromium Contaminated Soils.

The present study was conducted to evaluate the ability of a high biomass producing, drought tolerant succulent plant Mauritius hemp (Furcraea gigantea Vent.) for its tolerance to different levels of Cr (0, 25, 50, 100 and 200 mg Cr kg soil(-1)) and its potential for phytoremediation purposes. Based on the data on inhibition of the growth of plants with Cr, tolerance index and grade of growth inhibition, it was observed that the plant could tolerate up to 50 mg Cr kg (-1) soil. Absorption of Cr from soil to plant and its translocation into plant tissues were discussed in terms of bio concentration factor (BCF), transfer factor (TF), and translocation efficiency (TE%). Cr was mainly accumulated in the roots and exclusion of Cr was found to be the principal physiological tolerance mechanism followed by a marked increase in proline, ascorbic acid, total free amino acids in the leaf tissue and malic acid in the rhizosphere samples to counter Cr stress. Based on the tissue concentration of Cr (< 300 µg g(-1) in the leaves and TF<1), it was concluded that, Furcraea gigantea could not be considered a hyperaccumulator and therefore unsuitable for phytoextraction of Cr. Nevertheless, Furcraea gigantea could be a suitable candidate for phytostablization of Cr contaminated soils.

Multi-Block Bipartite Graph for Integrative Genomic Analysis.

Human diseases involve a sequence of complex interactions between multiple biological processes. In particular, multiple genomic data such as Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), DNA Methylation (DM), and their interactions simultaneously play an important role in human diseases. However, despite the widely known complex multi-layer biological processes and increased availability of the heterogeneous genomic data, most research has considered only a single type of genomic data. Furthermore, recent integrative genomic studies for the multiple genomic data have also been facing difficulties due to the high-dimensionality and complexity, especially when considering their intra- and inter-block interactions. In this paper, we introduce a novel multi-block bipartite graph and its inference methods, MB2I and sMB2I, for the integrative genomic study. The proposed methods not only integrate multiple genomic data but also incorporate intra/inter-block interactions by using a multi-block bipartite graph. In addition, the methods can be used to predict quantitative traits (e.g., gene expression, survival time) from the multi-block genomic data. The performance was assessed by simulation experiments that implement practical situations. We also applied the method to the human brain data of psychiatric disorders. The experimental results were analyzed by maximum edge biclique and biclustering, and biological findings were discussed.