Premiere of "Spaced Images with Noise and Lines" Performance at the Joinery, Dublin, at at Spatial Music Collective concert. Authors: Lyon, E. Keywords: (Spatial Audio, Electroacoustic Music, Computer Music, Algorithmic Music) Publication date: 07 Oct 2011

Performance at the Joinery, Dublin, at at Spatial Music Collective concert

Association between tortilla consumption and human urinary fumonisin B1 levels in a Mexican population

Fumonisins are mycotoxins produced by Fusarium spp. and commonly contaminate maize and maize products worldwide. Fumonisins are rodent carcinogens and have been associated with human esophageal cancer. However, the lack of a valid exposure biomarker has hindered both the assessment of human exposure and the evaluation of disease risk. A sensitive liquid chromatography-mass spectrometry method to measure urinary fumonisin B1 (FB1) following extraction on Oasis MAX cartridges was established and applied to urine samples from women in a cohort recruited in Morelos County, Mexico. Urinary FB1 was compared with dietary information on tortilla consumption. FB1 recovery in spiked samples averaged 94% as judged by deuterium-labeled FB1 internal standard. Urinary FB1 was determined in 75 samples from women selected based on low, medium, or high consumption of maize-based tortillas. The geometric mean (95% confidence interval) of urinary FB1 was 35.0 (18.8-65.2), 63.1 (36.8-108.2), and 147.4 (87.6-248.0) pg/mL and the frequency of samples above the detection limit (set at 20 pg FB1/mL urine) was 45%, 80%, and 96% for the low, medium, and high groups, respectively. Women with high intake had a 3-fold higher average FB1 levels compared with the "low intake" group (F = 7.3; P = 0.0015). Urinary FB1 was correlated with maize intake (P-trend = 0.001); the correlation remained significant after adjusting for age, education, and place of residence. This study suggests that measurement of urinary FB1 is sufficiently sensitive for fumonisin exposure assessment in human populations and could be a valuable tool in investigating the associated health effects of exposure.

The metastability of human UDP-galactose 4'-epimerase (GALE) is increased by variants associated with type III galactosemia but decreased by substrate and cofactor binding

Type III galactosemia is an inherited disease caused by mutations which affect the activity of UDP-galactose 4'-epimerase (GALE). We evaluated the impact of four disease-associated variants (p.N34S, p.G90E, p.V94M and p.K161N) on the conformational stability and dynamics of GALE. Thermal denaturation studies showed that wild-type GALE denatures at temperatures close to physiological, and disease-associated mutations often reduce GALE's thermal stability. This denaturation is under kinetic control and results partly from dimer dissociation. The natural ligands, NAD(+) and UDP-glucose, stabilize GALE. Proteolysis studies showed that the natural ligands and disease-associated variations affect local dynamics in the N-terminal region of GALE. Proteolysis kinetics followed a two-step irreversible model in which the intact protein is cleaved at Ala38 forming a long-lived intermediate in the first step. NAD(+) reduces the rate of the first step, increasing the amount of undigested protein whereas UDP-glucose reduces the rate of the second step, increasing accumulation of the intermediate. Disease-associated variants affect these rates and the amounts of protein in each state. Our results also suggest communication between domains in GALE. We hypothesize that, in vivo, concentrations of natural ligands modulate GALE stability and that it should be possible to discover compounds which mimic the stabilising effects of the natural ligands overcoming mutation-induced destabilization.

A decision support system for managing irrigation in agriculture

In this paper, an automatic Smart Irrigation Decision Support System, SIDSS, is proposed to manage irrigation in agriculture. Our system estimates the weekly irrigations needs of a plantation, on the basis of both soil measurements and climatic variables gathered by several autonomous nodes deployed in field. This enables a closed loop control scheme to adapt the decision support system to local perturbations and estimation errors. Two machine learning techniques, PLSR and ANFIS, are proposed as reasoning engine of our SIDSS. Our approach is validated on three commercial plantations of citrus trees located in the South-East of Spain. Performance is tested against decisions taken by a human expert.