Feasibility of infrared spectroscopy with pattern recognition techniques to identify a subpopulation of mares at risk of producing foals diagnosed with failure of transfer of passive immunity.

OBJECTIVE: To assess the feasibility of a serum-based test using infrared spectroscopy to identify a subpopulation of mares at risk of producing foals susceptible to failure of passive transfer of immunity (FPT) because of mare-associated factors. MATERIALS AND METHODS: Serum was collected from post-parturient mares (n = 126) and their foals at 24-72 h of age. A radial immunodiffusion IgG test was used to determine each foal's serum IgG concentration. Infrared absorbance spectra of dam sera were collected in the wave number range of 400-4000 cm(-1). Following data preprocessing, pattern recognition techniques were used to identify spectroscopic information capable of distinguishing between mares with FPT foals and those with normal foals. The sensitivity and specificity of infrared spectroscopy to detect risk-positive mares were calculated. RESULTS: Five wave number regions were identified as optimal for distinguishing between the two groups of mares: 740.9-785.2 cm(-1), 796.8-816.0 cm(-1), 970.4-993.5 cm(-1), 1371.6-1406.3 cm(-1) and 1632.0-1659.0 cm(-1). Based upon the infrared spectroscopic information within these discriminatory subregions, the spectra provided the risk status of the mares with a classification success rate of 81.0%. The sensitivity of the classification system was 85.7% and specificity was 80.0%. CONCLUSION: This preliminary study demonstrates that infrared spectra of dam serum have the potential to provide the basis for a new periparturient screening method for a subpopulation of mares at risk of having a foal susceptible to FPT. Further development may provide an economic and rapid technique for the pre-parturient assessment of mares.

Tolerance to analog hardware of on-chip learning in backpropagation networks.

In this paper we present results of simulations performed assuming both forward and backward computation are done on-chip using analog components. Aspects of analog hardware studied are component variability, limited voltage ranges, components (multipliers) that only approximate the computations in the backpropagation algorithm, and capacitive weight decay. It is shown that backpropagation networks can learn to compensate for all these shortcomings of analog circuits except for zero offsets, and the latter are correctable with minor circuit complications. Variability in multiplier gains is not a problem, and learning is still possible despite limited voltage ranges and function approximations. Fixed component variation from fabrication is shown to be less detrimental to learning than component variation due to noise. Weight decay is tolerable provided it is sufficiently small, which implies frequent refreshing by rehearsal on the training data or modest cooling of the circuits. The former approach allows for learning nonstationary problem sets.