Use of a novel micro-fluidic device to create arrays for multiplex analysis of large and small molecular weight compounds by surface plasmon resonance

There is an increasing demand to develop biosensor monitoring devices capable of biomarker profiling for predicting animal adulteration and detecting multiple chemical contaminants or toxins in food produce. Surface plasmon resonance (SPR) biosensors are label free detection systems that monitor the binding of specific biomolecular recognition elements with binding partners. Essential to this technology are the production of biochips where a selected binding partner, antibody, biomarker protein or low molecular weight contaminant, is immobilised. A micro-fluidic immobilisation device allowing the covalent attachment of up to 16 binding partners in a linear array on a single surface has been developed for compatibility with a prototype multiplex SPR analyser.

The immobilisation unit and multiplex SPR analyser were respectively evaluated in their ability to be fit-for-purpose for binding partner attachment and detection of high and low molecular weight molecules. The multiplexing capability of the dual technology was assessed using phycotoxin concentration analysis as a model system. The parent compounds of four toxin groups were immobilised within a single chip format and calibration curves were achieved. The chip design and SPR technology allowed the compartmentalisation of the binding interactions for each toxin group offering the added benefit of being able to distinguish between toxin families and perform concentration analysis. This model is particularly contemporary with the current drive to replace biological methods for phycotoxin screening.

Chronic treatment with a stable obestatin analogue significantly alters plasma triglyceride levels but fails to influence food intake, fluid intake, body weight, or body composition in rats.

Obestatin (OB(1-23) is a 23 amino acid peptide encoded on the preproghrelin gene, originally reported to have metabolic actions related to food intake, gastric emptying and body weight. The biological instability of OB(1-23) has recently been highlighted by studies demonstrating its rapid enzymatic cleavage in a number of biological matrices. We assessed the stability of both OB(1-23) and an N-terminally PEGylated analogue (PEG-OB(1-23)) before conducting chronic in vivo studies. Peptides were incubated in rat liver homogenate and degradation monitored by LC-MS. PEG-OB(1-23) was approximately 3-times more stable than OB(1-23). Following a 14 day infusion of Sprague Dawley rats with 50 mol/kg/day of OB(1-23) or a N-terminally PEGylated analogue (PEG-OB(1-23)), we found no changes in food/fluid intake, body weight and plasma glucose or cholesterol between groups. Furthermore, morphometric liver, muscle and white adipose tissue (WAT) weights and tissue triglyceride concentrations remained unaltered between groups. However, with stabilised PEG-OB(1-23) we observed a 40% reduction in plasma triglycerides. These findings indicate that PEG-OB(1-23) is an OB(1-23) analogue with significantly enhanced stability and suggest that obestatin could play a role in modulating physiological lipid metabolism, although it does not appear to be involved in regulation of food/fluid intake, body weight or fat deposition.

Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Effect of target slaughter weight on production efficiency, carcass traits and behaviour of restrictively-fed gilts and intact male finisher pigs

The effect of 3 slaughter weights (85.95 or 105 kg) on performance and carcass traits of 481 pigs in single-gender groups of 13 (18 groups of gilts and 19 groups of intact males) was evaluated. Pigs (39.5 +/- 3.3 kg) were fed a liquid diet 3 times daily in a long trough. The behaviour of pigs slaughtered at 105 kg was recorded at 50, 60 and 70 days after the start of the experiment (5 groups of gilts and 4 groups of intact males). Behaviour (active, inactive, feeding) and posture (standing, lying, dog-sitting) of all pigs was recorded at 5-min intervals for 30 min prior to and 1 h after each feeding event. Slaughtering pigs at 95 kg and 105 kg delayed production by 7 and 16 days, respectively, compared to slaughtering at 85 kg (P0.05). Muscle depth increased with increasing slaughter weight (P