Effect of Temperature and Ionic Substitutions on the Tegumental Potentials of Protoscoleces of Echinococcus granulosus.

The protoscolex (PSC) is generated by asexual reproduction at the larval stage of taeniid Echinococcus granulosus that causes cystic echinococcosis or hydatidosis, a worldwide zoonosis. The PSC is enveloped by a complex cellular syncytial tegument responsible for ionic movements and the hydroelectrolytic balance of the parasite. We recently reported on two electrical potentials in bovine lung protoscoleces (PSCs) that reflect differences in ionic movements between the parasite's invaginated and evaginated developmental stages. Here, we explored the effect of temperature and ionic substitutions on the tegumental potentials of bovine lung PSCs of Echinococcus granulosus by microelectrode impalements. We observed that the transient peak potential was temperature-dependent, consistent with an active transport component in the invaginated state only. Further changes in the electrical potentials by high K+ depolarization, low external Ca2+, and addition of the diuretic amiloride are in agreement with the presence of a Ca2+-sensitive cation-selective electrodiffusional pathway in the outer surface of the parasite. Variations in electrical potential differences through the tegument provide an accessible and valuable parameter for studying ionic transport mechanisms and, therefore, potential targets for developing novel antiparasitic drugs.

Electrical potentials of protoscoleces of the cestode Echinococcus granulosus from bovine origin.

Larval stages of taeniid Echinococcus granulosus are the infective forms of cystic echinococcosis or hydatidosis, a worldwide zoonosis. The protoscolex that develops into the adult form in the definitive host is enveloped by a complex cellular syncytial tegument, where all metabolic interchange takes place. Little information is available as to the electrical activity of the parasite in this developmental stage. To gain insight into the electrical activity of the parasite at the larval stage, we conducted microelectrode impalements of bovine lung protoscoleces (PSCs) of Echinococcus granulosus in standard saline solution. We observed two distinct intra-parasitic potentials, a transient peak potential, and a stable second potential, most likely representing tegumental and intra-parasitic extracellular space electrical potential differences. These values changed on the developmental status of the parasite, its anatomical regions, or time course after harvesting. Changes in electrical potential differences of the parasite provide an accessible and valuable parameter for the study of transport mechanisms and potential targets for developing novel antiparasitic therapeutics.

Self-organizing ARTMAP rule discovery.

The self-organizing ARTMAP rule discovery (SOARD) system derives relationships among recognition classes during online learning. SOARD training on input/output pairs produces the basic competence of direct recognition of individual class labels for new test inputs. As a typical supervised system, it learns many-to-one maps, which recognize different inputs (Spot, Rex) as belonging to one class (dog). As an ARTMAP system, it also learns one-to-many maps, allowing a given input (Spot) to learn a new class (animal) without forgetting its previously learned output (dog), even as it corrects erroneous predictions (cat). As it learns individual input/output class predictions, SOARD employs distributed code representations that support online rule discovery. When the input Spot activates the classes dogand animal, confidence in the rule dog→animal begins to grow. When other inputs simultaneously activate classes cat and animal, confidence in the converse rule, animal→dog, decreases. Confidence in a self-organized rule is encoded as the weight in a path from one class node to the other. An experience-based mechanism modulates the rate of rule learning, to keep inaccurate predictions from creating false rules during early learning. Rules may be excitatory or inhibitory so that rule-based activation can add missing classes and remove incorrect ones. SOARD rule activation also enables inputs to learn to make direct predictions of output classes that they have never experienced during supervised training. When input Rex activates its learned class dog, the rule dog→animal indirectly activates the output class animal. The newly activated class serves as a teaching signal which allows input Rex to learn direct activation of the output class animal. Simulations using small-scale and large-scale datasets demonstrate functional properties of the SOARD system in both spatial and time-series domains.