This paper reports some results of an on-going project using neural network modelling and learning techniques to search for and decode nonlinear regularities in asset price movements. We focus here on the case of IBM common stock daily returns. Having to deal with the salient features of economic data highlights the role to be played by statistical inference and requires modifications to standard learning techniques which may prove useful in other contexts.

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The stomach acts as a barrier to ingested microbes, thereby influencing the microbial ecology of the entire gastrointestinal (GI) tract. The stomach microbiota and the role of human host and environmental factors, such as health status or medications, in shaping its composition remain largely unknown. We sought to characterize the bacterial and fungal microbiota in the stomach fluid in order to gain insights into the role of the stomach in GI homeostasis. Gastric fluid was collected from 25 patients undergoing clinically indicated upper endoscopy. DNA isolates were used for PCR amplification of bacterial 16S ribosomal RNA (rRNA) genes and fungal internal transcribed spacers (ITS). RNA isolates were used for 16S rRNA cDNA generation and subsequent PCR amplification. While all stomach fluid samples are dominated by the phyla Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria and Fusobacteria (499% of sequence reads), the transcriptionally active microbiota shows significant reduction in Actinobacteria (34%) and increase in Campylobacter (444%) (Po0.003), specifically the oral commensal and suspected intestinal pathogen Campylobacter concisus. Bacterial but not fungal diversity is reduced by antibiotic treatment (28%; Po0.02), immunosuppression in transplant recipients and HIV/AIDS patients (42%; Po0.001) and gastric fluid pH 44 (70%; Po0.05). Immunosuppression correlates with decreased abundance of Prevotella (24%), Fusobacterium (2%) and Leptotrichia (6%) and increased abundance of Lactobacillus (3844%) (Po0.003). We have generated the first in-depth characterization of the human gastric fluid microbiota, using bacterial 16S rRNA gene and transcript, and fungal ITS amplicon sequencing and provide evidence for a significant impact of the host immune status on its composition with likely consequences for human health.

Anaemia in pregnancy in developing countries continues to be a public health problem of significant proportion. At least 50% of the anaemia has been blamed on iron deficiency. In populations where chronic inflammation and iron deficiency anaemia coexist, the criteria to accurately define iron status are not always clear. Similarly, in pregnancy, with marked physiological changes, cut-off points for biochemical parameters need to be re-examined. In this study we examined the diagnostic accuracy of iron parameters including mean cellular volume (MCV), serum iron, transferrin, total iron binding capacity (TIBC) and its saturation, zinc protoporphyrin (ZPP), ferritin and serum transferrin receptor (TfR) for the assessment of iron status in a population of anaemic pregnant women in Malawi. Stained bone marrow aspirates were used as the standard for comparison. Results show that for the purpose of screening, serum ferritin is the best single indicator of storage iron provided a cut-off point of 30 microg/l is used. A number of other commonly used parameters of iron status were shown to have limited diagnostic accuracy. Logistic regression was used to obtain mathematical models for the prediction of bone marrow iron status using a combination of available parameters.

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