Leptospiral antibodies in wild boars (Sus scrofa) bred in Brazil

In the recent years, the wild boar (Sus scrofa) trade has increased in Brazil. The present study aimed to detect Leptospira spp. infection in 308 blood samples from wild boars bred in São Paulo state, Brazil. The microscopic agglutination test (MAT) was performed using 29 serovars. Sixty three (63; 20.45 percent) animals tested positive against the serovars Hardjo (29/63; 46.0 percent), Copenhageni (11/63; 17.4 percent), Pomona (8/63; 12.7 percent), Pyrogenes (4/63; 6.3 percent), Wolfii (4/63; 6.3 percent), Autumnalis (3/63; 4.7 percent), Icterohaemorraghiae (2/63; 3.1 percent), and Hardjo-miniswajezak (1/63; 15.8 percent). These results indicate that captive wild boars were infected with Leptospira spp. and may represent a source of infection to humans and other animals.

Dissociation of the effects of bilateral lesions of the dorsal hippocampus and parietal cortex on path integration in the rat.

Rodents are able to rely on self-motion (idiothetic) cues and navigate toward a reference place by path integration. The authors tested the effects of dorsal hippocampal and parietal lesions in a homing task to dissociate the respective roles of the hippocampus and the parietal cortex in path integration. Hippocampal rats exhibited a strong deficit in learning the basic task. Parietal rats displayed a performance impairment as a function of the complexity of their outward paths when the food was placed at varying locations. These results suggest that the parietal cortex plays a specific role in path integration and in the processing of idiothetic information, whereas the hippocampus is involved in the calibration of space used by the path integration system.

Competitive Hebbian learning and the hippocampal place cell system: modeling the interaction of visual and path integration cues.

The hippocampus has long been thought essential for implementing a cognitive map of the environment. However, almost 30 years since place cells were found in rodent hippocampal field CA1, it is still unclear how such an allocentric representation arises from an ego-centrically perceived world. By means of a competitive Hebbian learning rule responsible for coding visual and path integration cues, our model is able to explain the diversity of place cell responses observed in a large set of electrophysiological experiments with a single fixed set of parameters. Experiments included changes observed in place fields due to exploration of a new environment, darkness, retrosplenial cortex inactivation, and removal, rotation, and permutation of landmarks. To code for visual cues for each landmark, we defined two perceptual schemas representing landmark bearing and distance information over a linear array of cells. The information conveyed by the perceptual schemas is further processed through a network of adaptive layers which ultimately modulate the resulting activity of our simulated place cells. In path integration terms, our system is able to dynamically remap a bump of activity coding for the displacement of the animal in relation to an environmental anchor. We hypothesize that path integration information is computed in the rodent posterior parietal cortex and conveyed to the hippocampus where, together with visual information, it modulates place cell activity. The resulting network yields a more direct treatment of partial remapping of place fields than other models. In so doing, it makes new predictions regarding the nature of the interaction between visual and path integration cues during new learning and when the system is challenged with environmental changes.

Hycones: a hybrid approach to designing decision support systems.

Hycones II is a tool that facilitates the construction of hybrid connectionist expert systems to solve problems in classification. Hycones II provides a hybrid system that integrates frames with three types of neural network: the "combinatorial" neural model (CNM), the "Fuzzy ARTMAP" model, and the "Semantic ART" (SMART) model, a combination of the CNM and Fuzzy ART-MAP. We will compare the ability of these three models to solve diagnostic problems in two medical domains.

Hybrid systems: a promising solution for better decision support tools.

This paper describes HYCONES II, a tool to enable construction of tightly coupled Hybrid Connectionist Expert Systems to solve classification problems. HYCONES II integrates frames with neural networks in its hybrid knowledge base. Three neural network models (CNM, the Fuzzy ARTMAP, and the Semantic ART - SMART) are available to the knowledge engineer. SMART is a new model, introduced by this research group and based on a combination of two previous models. The validation section compares the performance of these three neural models to solve diagnostic problems in two medical domains. Finally, the present status of this project and its future developments are presented.

HYCONES II: a tool to build hybrid connectionist expert systems.

This paper describes HYCONES II--a tool to enable the construction of hybrid connectionist expert systems to solve classification problems. HYCONES II offers to the knowledge engineer a hybrid knowledge base that integrates frames with three different neural network models: the combinatorial neural model--CNM, the Fuzzy ARTMAP and the Semantic ART--SMART models. The latter is a new model, introduced by this paper, based on a combination of the two previous models. The validation section compares the performance of these three neural models to solve diagnostic problems in two medical domains. This paper also presents HYCONES II knowledge representation features, built in the symbolic component of its hybrid knowledge-base, to deal and represent fuzzy medical variables. Finally, the present status and future developments of the project are presented.