Plasma brain-derived neurotrophic factor levels, learning capacity and cognition in patients with first episode psychosis

Background: Cognitive impairments are seen in first psychotic episode (FEP) patients. The neurobiological underpinnings that might underlie these changes remain unknown. The aim of this study is to investigate whether Brain Derived Neurotrophic Factor (BDNF) levels are associated with cognitive impairment in FEP patients compared with healthy controls. Methods: 45 FEP patients and 45 healthy controls matched by age, gender and educational level were selected from the Basque Country area of Spain. Plasma BDNF levels were assessed in healthy controls and in patients. A battery of cognitive tests was applied to both groups, with the patients being assessed at 6 months after the acute episode and only in those with a clinical response to treatment. Results: Plasma BDNF levels were altered in patients compared with the control group. In FEP patients, we observed a positive association between BDNF levels at six months and five cognitive domains (learning ability,immediate and delayed memory, abstract thinking and processing speed) which persisted after controlling for medications prescribed, drug use, intelligence quotient (IQ) and negative symptoms. In the healthy control group, BDNF levels were not associated with cognitive test scores. Conclusion: Our results suggest that BDNF is associated with the cognitive impairment seen after a FEP. Further investigations of the role of this neurotrophin in the symptoms associated with psychosis onset are warranted.

Using the system dynamics paradigm in teaching and learning technological university subjets

2nd International Conference on Education and New Learning Technologies

Cooperative learning strategies to teach nutrition to geriatric nursing staff

[EN] The objective of this study was to test the hypothesis that cooperative learning strategies will help to increase nutrition knowledge of nurses and nursing assistants caring for the elderly in different institutional communities of the Basque Country, Spain. The target population was a sample of volunteers, 16 nurses and 28 nursing assistants. Training consisted of 12 nutrition education sessions using cooperative strategies conducted over a period of 3 consecutive weeks. The assessment instruments included two pretest and two posttest questionnaires with questions selected in multiplechoice format. The first questionnaire was about general knowledge of applied nutrition (0-88 point scale) and the second one on geriatric nutrition knowledge (0-18 point scale). Data were analyzed using SPSS vs. 11.0. The outcomes indicated a significant increase in general nutrition knowledge (difference between the pre- and posttest mean score: 14.5±10.1; P<0.001) and in geriatric nutrition knowledge for all participants (difference between the pre- and post-test mean score: 4.6±4.6; P<0.001). So the results indicated that cooperative learning strategies could improve the nutrition knowledge of nursing staff. Additionally, the results of this study provide direction to continuing nutrition education program planners regarding appropriate content and methodology for programs.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

A Data Dropout Compensation Algorithm Based on the Iterative Learning Control Methodology for Discrete-Time Systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.

Assessment of faculty training program for the use of e-learning platforms: analysis of current use

5th International Conference on Education and New Learning Technologies (Barcelona, Spain. 1-3 July, 2013)