Early non-invasive detection of breast cancer using exhaled breath and urine analysis.

The main focus of this pilot study is to develop a statistical approach that is suitable to model data obtained by different detection methods. The methods used in this study examine the possibility to detect early breast cancer (BC) by exhaled breath and urine samples analysis. Exhaled breath samples were collected from 48 breast cancer patients and 45 healthy women that served as a control group.

Modelling the removal of volatile pollutants under transient conditions in a two-stage bioreactor using artificial neural networks.

A two-stage biological waste gas treatment system consisting of a first stage biotrickling filter (BTF) and second stage biofilter (BF) was tested for the removal of a gas-phase methanol (M), hydrogen sulphide (HS) and α-pinene (P) mixture. The bioreactors were tested with two types of shock loads, i.e.

Transient-state studies and neural modeling of the removal of a gas-phase pollutant mixture in a biotrickling filter.

The removal efficiency (RE) of gas-phase hydrogen sulfide (H), methanol (M) and α-pinene (P) in a biotrickling filter (BTF) was modeled using artificial neural networks (ANNs). The inlet concentrations of H, M, P, unit flow and operation time were used as the model inputs, while the outputs were the RE of H, M and P, respectively. After testing and validating the results, an optimal network topology of 5-8-3 was obtained.

Oviposition response of the moth Lobesia botrana to sensory cues from a host plant.

The grapevine moth Lobesia botrana is a generalist insect herbivore and grapevine is one of its hosts. Previous studies have shown that insects use their olfactory abilities to locate hosts from a distance; whereas contact chemoreception mediates the stimulation of oviposition after landing. Little is known about the role of olfaction and its interactions with contact chemoreception and vision once the insect lands on the plant.

An adaptive method for industrial hydrocarbon flame detection.

An adaptive method for an infrared (IR) hydrocarbon flame detection system is presented. The model makes use of joint time-frequency analysis (JTFA) for feature extraction and the artificial neural networks (ANN) for training and classification. Multiple ANNs are trained independently on a computer, using the backpropagation conjugate-gradient (CG) method, with input data collected from various flame and non-flame nuisance signals at four different IR wavelengths.

The use of artificial neural networks in prediction of congenital CMV outcome from sequence data.

A large number of CMV strains has been reported to circulate in the human population, and the biological significance of these strains is currently an active area of research. The analysis of complex genetic information may be limited using conventional phylogenetic techniques. We constructed artificial neural networks to determine their feasibility in predicting the outcome of congenital CMV disease (defined as presence of CMV symptoms at birth) based on two data sets: 54 sequences of CMV gene UL144 obtained from 54 amniotic fluids of women who contracted acute CMV infection during their pregnancy, and 80 sequences of 4 genes (US28, UL144, UL146 and UL147) obtained from urine, saliva or blood of 20 congenitally infected infants that displayed different outcomes at birth.