Assessment of Preclinical Learning on Oral Surgery Using Three Instructional Strategies

The purpose of this investigation was to evaluate three learning methods for teaching basic oral surgical skills Thirty predoctoral dental students without any surgical knowledge or previous surgical experience were divided Into three groups (n=10 each) according to instructional strategy Group 1, active learning Group 2, text reading only, and Group 3, text reading and video demonstration After instruction, the apprentices were allowed to practice incision dissection and suture maneuvers in a bench learning model During the students' performance, a structured practice evaluation test to account for correct or incorrect maneuvers was applied by trained observers Evaluation tests were repeated after thirty and sixty days Data from resulting scores between groups and periods were considered for statistical analysis (ANOVA and Tukey Kramer) with a significant level of a=0 05 Results showed that the active learning group presented the significantly best learning outcomes related to immediate assimilation of surgical procedures compared to other groups All groups results were similar after sixty days of the first practice Assessment tests were fundamental to evaluate teaching strategies and allowed theoretical and proficiency learning feedbacks Repetition and interactive practice promoted retention of knowledge on basic oral surgical skills

Knowledge and practices of medical students to prevent tuberculosis transmission in Rio de Janeiro, Brazil

Objectives. To describe knowledge, practices, and associated factors of medical students to prevent transmission of tuberculosis (TB) in five medical schools. Methods. Cross-sectional survey of undergraduate medical students in preclinical and in early and late clinical years. Information was obtained on sociodemographic profile, previous lectures on TB, knowledge about TB transmission, exposure to patients with active pulmonary TB, and use of respiratory protective masks. Results. Among 1 094 respondents, 575 (52.6%) correctly answered that coughing, speaking, and sneezing can transmit TB. Early [adjusted odds ratio = 4.0 (3.0, 5.5)] and late [adjusted odds ratio = 4.2 (3.1, 5.8)] clinical years were associated with correct answers, but having had previous lectures on TB was not. Among those who had previous lectures on TB, the rate of correct answers increased from 42.1% to 61.6%. Among 332 medical students who reported exposure to TB patients, 194 (58.4%) had not used protective masks. More years of clinical experience was associated with the use of masks [adjusted odds ratio = 2.9 (1.4, 6.1)], while knowledge was inversely associated with the use of masks [adjusted odds ratio = 0.4 (0.2, 0.6)]. Conclusions. Many medical students are not aware of the main routes of TB infection, and lectures on TB are not sufficient to change knowledge and practices. Regardless of knowledge about TB transmission, students engage in risky behaviors: more than two-thirds do not use a protective mask when examining an active TB case. We suggest innovative, effective active learning experiences to change this scenario.

Partial spectral projected gradient method with active-set strategy for linearly constrained optimization

A method for linearly constrained optimization which modifies and generalizes recent box-constraint optimization algorithms is introduced. The new algorithm is based on a relaxed form of Spectral Projected Gradient iterations. Intercalated with these projected steps, internal iterations restricted to faces of the polytope are performed, which enhance the efficiency of the algorithm. Convergence proofs are given and numerical experiments are included and commented. Software supporting this paper is available through the Tango Project web page: http://www.ime.usp.br/similar to egbirgin/tango/.

Continuous GRASP with a local active-set method for bound-constrained global optimization

Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a hybrid heuristic-based on the CGRASP and GENCAN methods-for finding approximate solutions for continuous global optimization problems subject to box constraints. Experimental results illustrate the relative effectiveness of CGRASP-GENCAN on a set of benchmark multimodal test functions.

Artigos científicos como recurso didático no ensino superior de Química

Chemistry teachers increasingly use research articles in their undergraduate courses. This trend arises from current pedagogical emphasis on active learning and scientific process. In this paper, we describe some educational experiences on the use of research articles in chemistry higher education. Additionally, we present our own conclusions on the use of such methodology applied to a scientific communication course offered to undergraduate chemistry students at the University of São Paulo, Brazil.

Resource use pattern analysis for predicting resource availability in opportunistic grids

This work presents a method for predicting resource availability in opportunistic grids by means of use pattern analysis (UPA), a technique based on non-supervised learning methods. This prediction method is based on the assumption of the existence of several classes of computational resource use patterns, which can be used to predict the resource availability. Trace-driven simulations validate this basic assumptions, which also provide the parameter settings for the accurate learning of resource use patterns. Experiments made with an implementation of the UPA method show the feasibility of its use in the scheduling of grid tasks with very little overhead. The experiments also demonstrate the method`s superiority over other predictive and non-predictive methods. An adaptative prediction method is suggested to deal with the lack of training data at initialization. Further adaptative behaviour is motivated by experiments which show that, in some special environments, reliable resource use patterns may not always be detected. Copyright (C) 2009 John Wiley & Sons, Ltd.

Biochemistry of the Envenomation Response-a Generator Theme for Interdisciplinary Integration

The understanding of complex physiological processes requires information from many different areas of knowledge. To meet this interdisciplinary scenario, the ability of integrating and articulating information is demanded. The difficulty of such approach arises because, more often than not, information is fragmented through under graduation education in Health Sciences. Shifting from a fragmentary and deep view of many topics to joining them horizontally in a global view is not a trivial task for teachers to implement. To attain that objective we proposed a course herein described Biochemistry of the envenomation response aimed at integrating previous contents of Health Sciences courses, following international recommendations of interdisciplinary model. The contents were organized by modules with increasing topic complexity. The full understanding of the envenoming pathophysiology of each module would be attained by the integration of knowledge from different disciplines. Active-learning strategy was employed focusing concept map drawing. Evaluation was obtained by a 30-item Likert-type survey answered by ninety students; 84% of the students considered that the number of relations that they were able to establish as seen by concept maps increased throughout the course. Similarly, 98% considered that both the theme and the strategy adopted in the course contributed to develop an interdisciplinary view.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

Pulmonary involvement in pleural tuberculosis: How often does it mean disease activity?

Objective: To evaluate in chest X-rays and high-resolution computed tomographies of patients with pleural tuberculosis, the incidence of parenchymal and mediastinal lung lesions suggestive of active disease. Methods: Prospective study (2008-2009) evaluating the radiographic and tomographic abnormalities of 88 HIV-negative patients with pleural tuberculosis (unilateral effusion). The images were reviewed by 3 independent specialists, and the observed changes were classified according to previously established criteria: presence or absence of signs suggestive of disease activity, and nonspecific findings. Results: Abnormal changes were observed in chest X-rays of 22 (25%) patients and in the computed tomography of 55 (63%). Images compatible with active pulmonary tuberculosis were detected by radiography in 9 (10%) patients and by tomography in 38 (43%). Only 4 (4.5%) patients had tomography images suggestive of residual disease. Conclusion: The present study demonstrates that pulmonary involvement is quite common in pleural tuberculosis. This finding is mainly observed in high-resolution computed tomography and has important epidemiological implications, since patients with pleural tuberculosis are significant sources of infection and disease dissemination. (C) 2011 Elsevier Ltd. All rights reserved.

Semantic mapping using mobile robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.

Using metrics from complex networks to evaluate machine translation

Establishing metrics to assess machine translation (MT) systems automatically is now crucial owing to the widespread use of MT over the web. In this study we show that such evaluation can be done by modeling text as complex networks. Specifically, we extend our previous work by employing additional metrics of complex networks, whose results were used as input for machine learning methods and allowed MT texts of distinct qualities to be distinguished. Also shown is that the node-to-node mapping between source and target texts (English-Portuguese and Spanish-Portuguese pairs) can be improved by adding further hierarchical levels for the metrics out-degree, in-degree, hierarchical common degree, cluster coefficient, inter-ring degree, intra-ring degree and convergence ratio. The results presented here amount to a proof-of-principle that the possible capturing of a wider context with the hierarchical levels may be combined with machine learning methods to yield an approach for assessing the quality of MT systems. (C) 2010 Elsevier B.V. All rights reserved.

Active Dendrites Enhance Neuronal Dynamic Range

Since the first experimental evidences of active conductances in dendrites, most neurons have been shown to exhibit dendritic excitability through the expression of a variety of voltage-gated ion channels. However, despite experimental and theoretical efforts undertaken in the past decades, the role of this excitability for some kind of dendritic computation has remained elusive. Here we show that, owing to very general properties of excitable media, the average output of a model of an active dendritic tree is a highly non-linear function of its afferent rate, attaining extremely large dynamic ranges (above 50 dB). Moreover, the model yields double-sigmoid response functions as experimentally observed in retinal ganglion cells. We claim that enhancement of dynamic range is the primary functional role of active dendritic conductances. We predict that neurons with larger dendritic trees should have larger dynamic range and that blocking of active conductances should lead to a decrease in dynamic range.

On the mass determination of super-Earths orbiting active stars: the CoRoT-7 system

Context. Star activity makes the mass determination of CoRoT-7b and CoRoT 7c uncertain. Investigators of the CoRoT team proposed several solutions, but all but one of them are larger than the initial determinations of 4.8 +/- 0.8 M(Earth) for CoRoT-7b and 8.4 +/- 0.9 M(Earth) for CoRoT 7c. Aims. This investigation uses the excellent HARPS radial velocity measurements of CoRoT-7 to redetermine the planet masses and to explore techniques for determining mass and orbital elements of planets discovered around active stars when the relative variation in the radial velocity due to the star activity cannot be considered as just noise and can exceed the variation due to the planets. Methods. The main technique used here is a self-consistent version of the high-pass filter used by Queloz et al. (2009, A&A, 506, 303) in the first mass determination of CoRoT-7b and CoRoT-7c. The results are compared to those given by two alternative techniques: (1) the approach proposed by Hatzes et al. (2010, A&A, 520, A93) using only those nights in which two or three observations were done; (2) a pure Fourier analysis. In all cases, the eccentricities are taken equal to zero as indicated by the study of the tidal evolution of the system. The periods are also kept fixed at the values given by Queloz et al. Only the observations done in the time interval BJD 2 454 847-873 are used because they include many nights with multiple observations; otherwise, it is not possible to separate the effects of the rotation fourth harmonic (5.91 d = P(rot)/4) from the alias of the orbital period of CoRoT-7b (0.853585 d). Results. The results of the various approaches are combined to give planet mass values 8.0 +/- 1.2 M(Earth) for CoRoT-7b and 13.6 +/- 1.4 M(Earth) for CoRoT 7c. An estimation of the variation of the radial velocity of the star due to its activity is also given. Conclusions. The results obtained with three different approaches agree to give higher masses than those in previous determinations. From the existing internal structure models they indicate that CoRoT-7b is a much denser super-Earth. The bulk density is 11 +/- 3.5 g cm(-3), so CoRoT-7b may be rocky with a large iron core.

Influence of the Operational Constraints in the Active Transmission Losses Allocation via Lagrange Multipliers

This paper presents an approach for the active transmission losses allocation between the agents of the system. The approach uses the primal and dual variable information of the Optimal Power Flow in the losses allocation strategy. The allocation coefficients are determined via Lagrange multipliers. The paper emphasizes the necessity to consider the operational constraints and parameters of the systems in the problem solution. An example, for a 3-bus system is presented in details, as well as a comparative test with the main allocation methods. Case studies on the IEEE 14-bus systems are carried out to verify the influence of the constraints and parameters of the system in the losses allocation.

Accelerating autonomous learning by using heuristic selection of actions

This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim, we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant performance enhancement of the learning rate.