Biodiversity of human faecal bacteria isolated from phytic acid enriched chemostat fermenters

Background: Myo-inositol hexaphosphate (IP6) or phytic acid is found mostly in cereals and legumes and is thought to possess anti-carcinogenic properties. Aim: To isolate and identify faecal bacteria capable of phytic acid metabolism and to assess the effectiveness of prebiotics (dietary oligosaccharides, metabolised by selective colonic bacteria) in preserving the integrity of phytic acid. Methods: Faecal samples from three volunteers were used in continuous culture experiments under varying conditions of pH, substrate concentration and dilution rates, seventy three different isolates cultured at steady state were then screened for phytic acid metabolism and identified through partial sequencing of their 16S rRNA genes (16S ribosomal ribonucleic acid). Utilisation of phytic acid was also assessed in a continuous culture system enriched with prebiotic fructooligosaccharides (FOS). Results: Bacteroides spp., Clostridium spp. and facultatively anaerobic bacteria generally appeared to maintain viable counts in the presence of phytic acid. Bifidobacterium spp. and Lactobacillus spp. appeared less able to maintain viable counts in the presence of phytic acid. These results were confirmed by an increase in viable counts of Bacteroides spp., Clostridium spp. and a decrease in viable counts of Bifidobacterium spp. and Lactobacillus spp. once phytic acid was introduced to a FOS enriched continuous culture. Conclusions: The phytate metabolising biodiversity from the human large intestine does not appear to encompass major bacterial genera associated with beneficial or benign health effects (e.g. Lactobacillus spp. and Bifidobacterium spp).

Variation in root-associated phosphatase activities in wheat contributes to the utilization of organic P substrates in vitro, but does not explain differences in the P-nutrition of plants when grown in soils

To understand whether genotypic variation in root-associated phosphatase activities in wheat impacts on its ability to acquire phosphorus (P), various phosphatase activities of roots were measured in relation to the utilization of organic P substrates in agar, and the P-nutrition of plants was investigated in a range of soils. Root-associated phosphatase activities of plants grown in hydroponics were measured against different organic P substrates. Representative genotypes were then grown in both agar culture and in soils with differing organic P contents and plant biomass and P uptake were determined. Differences in the activities of both root-associated and exuded phosphodiesterase and phosphomonoesterase were observed, and were related to the P content of plants supplied with either ribonucleic acid or glucose 6-phosphate, respectively, as the sole form of P. When the cereal lines were grown in different soils, however, there was little relationship between any root-associated phosphatase activity and plant P uptake. This indicates that despite differences in phosphatase activities of cereal roots, such variability appears to play no significant role in the P-nutrition of the plant grown in soil, and that any benefit derived from the hydrolysis of soil organic P is common to all genotypes.

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.