Brief Psychotherapy assists patients to become aware and change their behavior when facing an immediate emotional conflict, and to implement a transformation process through actions of listening, observing, increasing awareness and making interventions. Therapeutic work employs tools and techniques to trigger a process of change, emphasizing cognitive and affective understanding. This article presents an approach that combines Psychology and Artificial Intelligence with the purpose of enhancing psychotherapy with computer-implemented tools. This approach highlights the intersection between these two knowledge areas and shows how machine intelligence can help to characterize affective areas, construct genograms, determine degree of differentiation of self, investigate cognitive interaction patterns, and achieve self-awareness and redefinition. The conceptual proposal was implemented by a web application, and a sample of computer-aided analysis is presented.

Neoproterozoic granitoid suites in southeastern Brazil are closely related to the different tectonic events. Petrography, geochemistry and structural features allow to separate these rocks into pre-, syn-, late and post-tectonic granitoids. The whole emplacement process lasted less than 100 Ma: from 595 Ma for the pre-tectonic granitoids, to 500 Ma for the post-tectonic ones. During this interval a 45 Ma magmatic quiet period (from 582 to 537 Ma) can also be characterized. Detailed field observations and mapping, coupled with petrological and geochemical observations, indicate an important role of the Archaean and Paleoproterozoic crust in the genesis of these granitoids, showing even evidences of some mantle interactions.

Background There are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Thus, its taxonomy is one of the most complex among Nematoda. Eggs are the parasitic structures most viewed in coprological analysis in both modern and ancient samples; consequently, their presence is indicative of positive diagnosis for infection. The structure of the egg could play a role in genera or species discrimination. Institutional biological collections are taxonomic repositories of specimens described and strictly identified by systematics specialists. Methods The present work aims to characterize eggs of capillariid species deposited in institutional helminth collections and to process the morphological, morphometric and ecological data using machine learning (ML) as a new approach for taxonomic identification. Specimens of 28 species and 8 genera deposited at Coleção Helmintológica do Instituto Oswaldo Cruz (CHIOC, IOC/FIOCRUZ/Brazil) and Collection de Nématodes Zooparasites du Muséum National d’Histoire Naturelle de Paris (MNHN/France) were examined under light microscopy. In the morphological and morphometric analyses (MM), the total length and width of eggs as well as plugs and shell thickness were considered. In addition, eggshell ornamentations and ecological parameters of the geographical location (GL) and host (H) were included. Results The performance of the logistic model tree (LMT) algorithm showed the highest values in all metrics compared with the other algorithms. Algorithm J48 produced the most reliable decision tree for species identification alongside REPTree. The Majority Voting algorithm showed high metric values, but the combined classifiers did not attenuate the errors revealed in each algorithm alone. The statistical evaluation of the dataset indicated a significant difference between trees, with GL + H + MM and MM only with the best scores. Conclusions The present research proposed a novel procedure for taxonomic species identification, integrating data from centenary biological collections and the logic of artificial intelligence techniques. This study will support future research on taxonomic identification and diagnosis of both modern and archaeological capillariids. Graphical abstract

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