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BackgroundLyme borreliosis (LB) is the most commonly reported vector-borne disease in north temperate regions worldwide, affecting an estimated 300,000 people annually in the United States alone. The incidence of LB is correlated with human exposure to its vector, the blacklegged tick (Ixodes scapularis). To date, attempts to model tick encounter risk based on environmental parameters have been equivocal. Previous studies have not considered (1) the differences between relative humidity (RH) in leaf litter and at weather stations, (2) the RH threshold that affects nymphal blacklegged tick survival, and (3) the time required below the threshold to induce mortality. We clarify the association between environmental moisture and tick survival by presenting a significant relationship between the total number of tick adverse moisture events (TAMEs - calculated as microclimatic periods below a RH threshold) and tick abundance each year.MethodsWe used a 14-year continuous statewide tick surveillance database and corresponding weather data from Rhode Island (RI), USA, to assess the effects of TAMEs on nymphal populations of I. scapularis. These TAMEs were defined as extended periods of time (>8 h below 82% RH in leaf litter). We fit a sigmoid curve comparing weather station data to those collected by loggers placed in tick habitats to estimate RH experienced by nymphal ticks, and compiled the number of historical TAMEs during the 14-year record.ResultsThe total number of TAMEs in June of each year was negatively related to total seasonal nymphal tick densities, suggesting that sub-threshold humidity episodes >8 h in duration naturally lowered nymphal blacklegged tick abundance. Furthermore, TAMEs were positively related to the ratio of tick abundance early in the season when compared to late season, suggesting that lower than average tick abundance for a given year resulted from tick mortality and not from other factors.ConclusionsOur results clarify the mechanism by which environmental moisture affects blacklegged tick populations, and offers the possibility to more accurately predict tick abundance and human LB incidence. We describe a method to forecast LB risk in endemic regions and identify the predictive role of microclimatic moisture conditions on tick encounter risk.

-The visualization of support vector machines in realistic settings is a difficult problem due to the high dimensionality of the typical datasets involved. However, such visualizations usually aid the understanding of the model and the underlying processes, especially in the biosciences. Here we propose a novel visualization technique of support vector machines based on unsupervised learning, specifically self-organizing maps. Conceptually, self-organizing maps can be thought of as neural networks that investigate a high-dimensional data space for clusters of data points and then project the clusters onto a two-dimensional map preserving the topologies of the original clusters as much as possible. This allows for the visualization of high-dimensional datasets together with their support vector models. With this technique we investigate a number of support vector machine visualization scenarios based on real world biomedical datasets.

Spectral features from specific regions in infrared spectra of organic molecules can consistently be attributed to certain functional groups. Artificial neural networks were employed as a pattern recognition tool to elucidate the relationships between functional groups and spectral features. The ability of these network models to predict the presence and absence of a variety of functional groups was evaluated. The sensitivity of the artificial neural network over the entire infrared spectral region was used to generate a spectral factor representation of the major information associated with each functional group. The resulting sensitivity factors were utilized in a much simpler model for functional group prediction. Ultimately, the presence of a functional group was predicted based on the dot product of an unknown spectrum with the corresponding sensitivity factor. A probability based on Bayes' theorem was assigned to each of the predictions. The prediction accuracies were greater than 90% for all 13 functional groups considered in the investigation.

Self-organizing maps are artificial neural networks designed for unsupervised machine learning. Here in this article, the authors introduce a new quality measure called the convergence index. The convergence index is a linear combination of map embedding accuracy and estimated topographic accuracy and since it reports a single statistically meaningful number it is perhaps more intuitive to use than other quality measures. The convergence index in the context of clustering problems was proposed by Ultsch as part of his fundamental clustering problem suite as well as real world datasets. First demonstrated is that the convergence index captures the notion that a SOM has learned the multivariate distribution of a training data set by looking at the convergence of the marginals. The convergence index is then used to study the convergence of SOMs with respect to the different parameters that govern self-organizing map learning. One result is that the constant neighborhood function produces better self-organizing map models than the popular Gaussian neighborhood function.