Tele-education on Leprosy: Evaluation of an Educational Strategy

The purpose of this research was to evaluate educational strategies applied to a tele-education leprosy course. The curriculum was for members of the Brazilian Family Health Team and was made available through the Sao Paulo Telehealth Portal. The course educational strategy was based on a constructivist learning model where interactivity was emphasized. Authors assessed motivational aspects of the course using the WebMAC Professional tool. Forty-eight healthcare professionals answered the evaluation questionnaire. Adequate internal consistency was achieved (Cronbach`s alpha = 0.79). More than 95% of queried items received good evaluations. Multidimensional analysis according to motivational groups of questions (STIMULATING, MEANINGFUL, ORGANIZED, EASY-TO-USE) showed high agreement. According to WebMAC`s criteria, it was considered an ""awesome course."" The tele-educational strategies implemented for leprosy disclosed high motivational scores.

Time course of haemodynamic, respiratory and inflammatory disturbances induced by experimental acute pulmonary polystyrene microembolism

Background and objective The time course of cardiopulmonary alterations after pulmonary embolism has not been clearly demonstrated and nor has the role of systemic inflammation on the pathogenesis of the disease. This study aimed to evaluate over 12 h the effects of pulmonary embolism caused by polystyrene microspheres on the haemodynamics, lung mechanics and gas exchange and on interleukin-6 production. Methods Ten large white pigs (weight 35-42 kg) had arterial and pulmonary catheters inserted and pulmonary embolism was induced in five pigs by injection of polystyrene microspheres (diameter similar to 300 mu mol l(-1)) until a value of pulmonary mean arterial pressure of twice the baseline was obtained. Five other animals received only saline. Haemodynamic and respiratory data and pressure-volume curves of the respiratory system were collected. A bronchoscopy was performed before and 12 h after embolism, when the animals were euthanized. Results The embolism group developed hypoxaemia that was not corrected with high oxygen fractions, as well as higher values of dead space, airway resistance and lower respiratory compliance levels. Acute haemodynamic alterations included pulmonary arterial hypertension with preserved systemic arterial pressure and cardiac index. These derangements persisted until the end of the experiments. The plasma interleukin-6 concentrations were similar in both groups; however, an increase in core temperature and a nonsignificant higher concentration of bronchoalveolar lavage proteins were found in the embolism group. Conclusion Acute pulmonary embolism induced by polystyrene microspheres in pigs produces a 12-h lasting hypoxaemia and a high dead space associated with high airway resistance and low compliance. There were no plasma systemic markers of inflammation, but a higher central temperature and a trend towards higher bronchoalveolar lavage proteins were found. Eur J Anaesthesiol 27:67-76 (C) 2010 European Society of Anaesthesiology.

Endoscopic Anatomy of the Pterygopalatine Fossa and the Transpterygoid Approach: Development of a Surgical Instruction Model

Introduction: The pterygopalatine fossa (PPF) is a narrow space located between the posterior wall of the antrum and the pterygoid plates. Surgical access to the PPF is difficult because of its protected position and its complex neurovascular anatomy. Endonasal approaches using rod lens endoscopes, however, provide better visualization of this area and are associated with less morbidity than external approaches. Our aim was to develop a simple anatomical model using cadaveric specimens injected with intravascular colored silicone to demonstrate the endoscopic anatomy of the PPF. This model could be used for surgical instruction of the transpterygoid approach. Methods: We dissected six PPF in three cadaveric specimens prepared with intravascular injection of colored material using two different injection techniques. An endoscopic endonasal approach, including a wide nasoantral window and removal of the posterior antrum wall, provided access to the PPF. Results: We produced our best anatomical model injecting colored silicone via the common carotid artery. We found that, using an endoscopic approach, a retrograde dissection of the sphenopalatine artery helped to identify the internal maxillary artery (IMA) and its branches. Neural structures were identified deeper to the vascular elements. Notable anatomical landmarks for the endoscopic surgeon are the vidian nerve and its canal that leads to the petrous portion of the internal carotid artery (ICA), and the foramen rotundum, and V2 that leads to Meckel`s cave in the middle cranial fossa. These two nerves, vidian and V2, are separated by a pyramidal shaped bone and its apex marks the ICA. Conclusion: Our anatomical model provides the means to learn the endoscopic anatomy of the PPF and may be used for the simulation of surgical techniques. An endoscopic endonasal approach provides adequate exposure to all anatomical structures within the PPF. These structures may be used as landmarks to identify and control deeper neurovascular structures. The significance is that an anatomical model facilitates learning the surgical anatomy and the acquisition of surgical skills. A dissection superficial to the vascular structures preserves the neural elements. These nerves and their bony foramina, such as the vidian nerve and V2, are critical anatomical landmarks to identify and control the ICA at the skull base.

Learning of shared attention in sociable robotics

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They Should be able to recognize human beings and each other, and to engage in social, interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction. behavior control and learning from environment. Learning processes described oil Science of Behavior Analysis may lead to the development of promising methods and Structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation. are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction. (C) 2009 Elsevier Inc. All rights reserved.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Opinion dynamics of learning agents: does seeking consensus lead to disagreement?

We study opinion dynamics in a population of interacting adaptive agents voting on a set of issues represented by vectors. We consider agents who can classify issues into one of two categories and can arrive at their opinions using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean perceptron to state their opinion. If the update occurs with information asynchronously exchanged among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society torn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. If the number of issues is large, the dynamics becomes trapped, the society does not evolve into factions and a distribution of moderate opinions is observed. The synchronous case is technically simpler and is studied by formulating the problem in terms of differential equations that describe the evolution of order parameters that measure the consensus between pairs of agents. We show that for a large number of issues and unidirectional information flow, global consensus is a fixed point; however, the approach to this consensus is glassy for large societies.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.