Induction of anti-chlamydial mucosal immunity by transcutaneous immunization is enhanced by topical application of GM-CSF

Transcutaneous immunization (TCI) involves the direct application of antigen plus adjuvant to skin, taking advantage of the large numbers of Langerhans cells and other resident skin dendritic cells, that process antigen then migrate to draining lymph nodes where immune responses are initiated. We have used this form of immunization to protect mice against genital tract and respiratory tract chlamydial infection. Protection was associated with local antibody responses in the vagina, uterus and lung as well as strong Th1 responses in the lymph nodes draining the reproductive tract and lungs respectively. In this study we show that topical application of GM-CSF to skin enhances the numbers and activation status of epidermal dendritic cells. Topical application of GM-CSF also increased the immune responses elicited by TCI. GM-CSF supplementation greatly increased cytokine (IFNgamma and IL-4) gene expression in lymph node and splenic cells compared to cells from animals immunized without GM-CSF. IgG responses in serum, uterine lavage and bronchoalveolar lavage and IgA responses in vaginal lavage were also increased by topical application of GM-CSF. The studies show that TCI induces protection against genital and respiratory tract chlamydial infections and that topical application of cytokines such as GM-CSF can enhance TCI-induced antibody and cell-mediated immunity.

Measuring and decomposing changes in agricultural productivity, nitrogen use efficiency and cumulative exergy efficiency: Application to OECD agriculture

This paper uses an aggregate quantity space to decompose the temporal changes in nitrogen use efficiency and cumulative exergy use efficiency into changes of Moorsteen–Bjurek (MB) Total Factor Productivity (TFP) changes and changes in the aggregate nitrogen and cumulative exergy contents. Changes in productivity can be broken into technical change and changes in various efficiency measures such as technical efficiency, scale efficiency and residual mix efficiency. Changes in the aggregate nitrogen and cumulative exergy contents can be driven by changes in the quality of inputs and outputs and changes in the mixes of inputs and outputs. Also with cumulative exergy content analysis, changes in the efficiency in input production can increase or decrease the cumulative exergy transformity of agricultural production. The empirical study in 30 member countries of the Organisation for Economic Co-operation Development from 1990 to 2003 yielded some important findings. The production technology progressed but there were reductions in technical efficiency, scale efficiency and residual mix efficiency levels. This result suggests that the production frontier had shifted up but there existed lags in the responses of member countries to the technological change. Given TFP growth, improvements in nutrient use efficiency and cumulative exergy use efficiency were counteracted by reductions in the changes of the aggregate nitrogen contents ratio and aggregate cumulative exergy contents ratio. The empirical results also confirmed that different combinations of inputs and outputs as well as the quality of inputs and outputs could have more influence on the growth of nutrient and cumulative exergy use efficiency than factors that had driven productivity change. Keywords: Nutrient use efficiency; Cumulative exergy use efficiency; Thermodynamic efficiency change; Productivity growth; OECD agriculture; Sustainability

Application of higher order spectra to identify epileptic EEG

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.