Detecção de outliers espaciais: refinamento de similaridade e desempenho

Pós-graduação em Ciência da Computação - IBILCE

Does proprioceptive system stimulation improve sit-to-walk performance in healthy young adults?

[Purpose] Sit-to-walk performance is linked to proper proprioceptive information processing. Therefore, it is believed that an increase of proprioceptive inflow (using muscle vibration) might improve sit-to-walk performance. However, before testing muscle vibration effects on a frail population, assessment of its effects on healthy young people is necessary. Thus, the aim of this study was to investigate the effects of muscle vibration on sit-to-walk performance in healthy young adults. [Subjects and Methods] Fifteen young adults performed the sit-to-walk task under three conditions: without vibration, with vibration applied before movement onset, and with vibration applied during the movement. Vibration was applied bilaterally for 30 s to the tibialis anterior, rectus femoris, and upper trapezius muscles bellies. The vibration parameters were as follows: 120 Hz and 1.2 mm. Kinematics and kinetic data were assessed using a 3D motion capture system and two force plates. The coordinates of reflective markers were used to define the center-of-mass velocities and displacements. In addition, the first step spatiotemporal variables were assessed. [Results] No vibration effect was observed on any dependent variables. [Conclusion] The results show that stimulation of the proprioceptive system with local muscle vibration does not improve sit-towalk performance in healthy young adults.

Neuroglia and their roles in central respiratory control; an overview

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Como os sintomas de transtornos mentais e comportamentais influenciam a memória operacional de universitários?

Pós-graduação em Psicologia do Desenvolvimento e Aprendizagem - FC

Preferência por marcos em mapas 3D de sistema de navegação e guia de rota em automóvel

Pós-graduação em Ciências Cartográficas - FCT

Inteligência cibernética e uso de recursos semânticos na detecção de perfis falsos no contexto do Big Data

Pós-graduação em Ciência da Informação - FFC

Inteligência cibernética e uso de recursos semânticos na detecção de perfis falsos no contexto do Big Data

Pós-graduação em Ciência da Informação - FFC

Child Physical Abuse: Evaluating Psychological Risk Factors in Accused Caregivers

It was verified to what extent cognitive and affective/emotional variables could distinguish caregivers accused of committing physical abuse (G1) from those without physical abuse records (G2). The Child Abuse Potential Inventory (CAP), which is an instrument designed to assess psychological risk factors in caregivers, was used. A questionnaire on socio-demographic characterization and another on economic classification were also employed to equate the groups. G1 presented a greater potential risk than G2, higher levels of Distress, Rigidity, Problems with the Child and with Themselves, Problems with Others, and a lower level of Ego Strength. These variables contribute with the composition of physical abuse risk, since, in agreement with the Social Information Processing Model, they would be related to cognitive and affective basic processes which are veiled to the perceptions and evaluation/interpretations, associated to abusive parental behavior.

A graph-theoretical approach in brain functional networks. Possible implications in EEG studies

Abstract Background Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory, that is a mathematical representation of a network, which is essentially reduced to nodes and connections between them. Methods We used high-resolution EEG technology to enhance the poor spatial information of the EEG activity on the scalp and it gives a measure of the electrical activity on the cortical surface. Afterwards, we used the Directed Transfer Function (DTF) that is a multivariate spectral measure for the estimation of the directional influences between any given pair of channels in a multivariate dataset. Finally, a graph theoretical approach was used to model the brain networks as graphs. These methods were used to analyze the structure of cortical connectivity during the attempt to move a paralyzed limb in a group (N=5) of spinal cord injured patients and during the movement execution in a group (N=5) of healthy subjects. Results Analysis performed on the cortical networks estimated from the group of normal and SCI patients revealed that both groups present few nodes with a high out-degree value (i.e. outgoing links). This property is valid in the networks estimated for all the frequency bands investigated. In particular, cingulate motor areas (CMAs) ROIs act as ‘‘hubs’’ for the outflow of information in both groups, SCI and healthy. Results also suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property. In particular, a higher local efficiency El can be observed in the SCI patients for three frequency bands, theta (3-6 Hz), alpha (7-12 Hz) and beta (13-29 Hz). By taking into account all the possible pathways between different ROI couples, we were able to separate clearly the network properties of the SCI group from the CTRL group. In particular, we report a sort of compensatory mechanism in the SCI patients for the Theta (3-6 Hz) frequency band, indicating a higher level of “activation” Ω within the cortical network during the motor task. The activation index is directly related to diffusion, a type of dynamics that underlies several biological systems including possible spreading of neuronal activation across several cortical regions. Conclusions The present study aims at demonstrating the possible applications of graph theoretical approaches in the analyses of brain functional connectivity from EEG signals. In particular, the methodological aspects of the i) cortical activity from scalp EEG signals, ii) functional connectivity estimations iii) graph theoretical indexes are emphasized in the present paper to show their impact in a real application.

O envolvimento da atenção na codificação da informação visuoespacial integrada

O objetivo deste estudo foi investigar o envolvimento de recursos da atenção na codificação e manutenção da informação visual e espacial na memória de trabalho. Utilizou-se um paradigma de tarefas duplas em que uma tarefa primária de localização espacial foi realizada simultaneamente a uma tarefa atentiva secundária de discriminação de tons. O desempenho dos participantes (n = 20) na tarefa primária foi afetado pela presença e pela similaridade entre os tons da tarefa secundária, e também, pela instrução de priorizar uma ou outra tarefa. Os resultados indicam que recursos atentivos (do executivo central) estão envolvidos na codificação e na manutenção ativa da informação integrada na memória visuoespacial, assim como na manutenção dos objetivos das tarefas a serem realizadas simultaneamente.

Abuso físico infantil: avaliando fatores de risco psicológicos em cuidadores notificados

Verificou-se em que medida variáveis cognitivas e afetivas/emocionais diferenciariam cuidadores notificados por abusos físicos (G1) de cuidadores sem esse histórico (G2). O Child Abuse Potential Inventory (CAP) foi utilizado para avaliar fatores de risco psicológicos em cuidadores. Um Questionário de Caracterização sócio-demográfica e outro econômico também foram empregados para equiparar os grupos. G1 apresentou um potencial de risco superior a G2, e maiores níveis de Angústia, Rigidez, Problemas com a Criança e Consigo, Problemas com os Outros, e um menor nível de Força do Ego. Essas variáveis se articulam para compor o risco de abuso físico, pois segundo o Modelo do Processamento da Informação Social, remeteriam a processos básicos cognitivos/afetivos subjacentes a percepções e avaliações/interpretações, associados ao comportamento parental abusivo.

Spectral characteristics of the hippocampal LFP during contextual fear conditioning

OBJECTIVE: The hippocampus has an important role in the acquisition and recall of aversive memories. The objective of this study was to investigate the relationship among hippocampal rhythms. METHODS: Microeletrodes arrays were implanted in the hippocampus of Wistar rats. The animals were trained and tested in a contextual fear conditioning task. The training consisted in applying shocks in the legs. The memory test was performed 1 day (recent memory) or 18 days (remote memory) after training. We proposed a measure based on the FFT power spectrum, denominated "delta-theta ratio", to characterize the different behaviors (active exploration and freezing) and the memories types. RESULTS: The delta-theta ratio was able to distinguish recent and remote memories. In this study, the ratio for the 18-day group was smaller than for the 1-day group. Moreover, this measure was useful to distinguish the different behavior states active exploration and freezing. CONCLUSIONS: The results suggest delta-theta oscillations could reflect the demands on information processing during recent and remote memory recalls.

Wildfire prevention and management in a 3D virtual environment

[EN] This paper describes a wildfire forecasting application based on a 3D virtual environment and a fire simulation engine. A new open source framework is presented for the development of 3D graphics applications over large geographic areas offering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems community. The application includes a remote module that allows simultaneous connection of several users for monitoring a real wildfire event. The user is enabled to simulate and visualize a wildfire spreading on the terrain under conditions of spatial information on topography and fuels along with weather and wind files.

Design and implementation of bioinformatics tools for large scale genome annotation

The continuous increase of genome sequencing projects produced a huge amount of data in the last 10 years: currently more than 600 prokaryotic and 80 eukaryotic genomes are fully sequenced and publically available. However the sole sequencing process of a genome is able to determine just raw nucleotide sequences. This is only the first step of the genome annotation process that will deal with the issue of assigning biological information to each sequence. The annotation process is done at each different level of the biological information processing mechanism, from DNA to protein, and cannot be accomplished only by in vitro analysis procedures resulting extremely expensive and time consuming when applied at a this large scale level. Thus, in silico methods need to be used to accomplish the task. The aim of this work was the implementation of predictive computational methods to allow a fast, reliable, and automated annotation of genomes and proteins starting from aminoacidic sequences. The first part of the work was focused on the implementation of a new machine learning based method for the prediction of the subcellular localization of soluble eukaryotic proteins. The method is called BaCelLo, and was developed in 2006. The main peculiarity of the method is to be independent from biases present in the training dataset, which causes the over‐prediction of the most represented examples in all the other available predictors developed so far. This important result was achieved by a modification, made by myself, to the standard Support Vector Machine (SVM) algorithm with the creation of the so called Balanced SVM. BaCelLo is able to predict the most important subcellular localizations in eukaryotic cells and three, kingdom‐specific, predictors were implemented. In two extensive comparisons, carried out in 2006 and 2008, BaCelLo reported to outperform all the currently available state‐of‐the‐art methods for this prediction task. BaCelLo was subsequently used to completely annotate 5 eukaryotic genomes, by integrating it in a pipeline of predictors developed at the Bologna Biocomputing group by Dr. Pier Luigi Martelli and Dr. Piero Fariselli. An online database, called eSLDB, was developed by integrating, for each aminoacidic sequence extracted from the genome, the predicted subcellular localization merged with experimental and similarity‐based annotations. In the second part of the work a new, machine learning based, method was implemented for the prediction of GPI‐anchored proteins. Basically the method is able to efficiently predict from the raw aminoacidic sequence both the presence of the GPI‐anchor (by means of an SVM), and the position in the sequence of the post‐translational modification event, the so called ω‐site (by means of an Hidden Markov Model (HMM)). The method is called GPIPE and reported to greatly enhance the prediction performances of GPI‐anchored proteins over all the previously developed methods. GPIPE was able to predict up to 88% of the experimentally annotated GPI‐anchored proteins by maintaining a rate of false positive prediction as low as 0.1%. GPIPE was used to completely annotate 81 eukaryotic genomes, and more than 15000 putative GPI‐anchored proteins were predicted, 561 of which are found in H. sapiens. In average 1% of a proteome is predicted as GPI‐anchored. A statistical analysis was performed onto the composition of the regions surrounding the ω‐site that allowed the definition of specific aminoacidic abundances in the different considered regions. Furthermore the hypothesis that compositional biases are present among the four major eukaryotic kingdoms, proposed in literature, was tested and rejected. All the developed predictors and databases are freely available at: BaCelLo http://gpcr.biocomp.unibo.it/bacello eSLDB http://gpcr.biocomp.unibo.it/esldb GPIPE http://gpcr.biocomp.unibo.it/gpipe