Physiological modules for generating discrete and rhythmic movements: action identification by a dynamic recurrent neural network

In this study we employed a dynamic recurrent neural network (DRNN) in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane). We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others elliciting patterns of reciprocal activation operating in orthogonal directions.

Estimating relative abundance from catch and effort data, using neural networks

We develop and test a method to estimate relative abundance from catch and effort data using neural networks. Most stock assessment models use time series of relative abundance as their major source of information on abundance levels. These time series of relative abundance are frequently derived from catch-per-unit-of-effort (CPUE) data, using general linearized models (GLMs). GLMs are used to attempt to remove variation in CPUE that is not related to the abundance of the population. However, GLMs are restricted in the types of relationships between the CPUE and the explanatory variables. An alternative approach is to use structural models based on scientific understanding to develop complex non-linear relationships between CPUE and the explanatory variables. Unfortunately, the scientific understanding required to develop these models may not be available. In contrast to structural models, neural networks uses the data to estimate the structure of the non-linear relationship between CPUE and the explanatory variables. Therefore neural networks may provide a better alternative when the structure of the relationship is uncertain. We use simulated data based on a habitat based-method to test the neural network approach and to compare it to the GLM approach. Cross validation and simulation tests show that the neural network performed better than nominal effort and the GLM approach. However, the improvement over GLMs is not substantial. We applied the neural network model to CPUE data for bigeye tuna (Thunnus obesus) in the Pacific Ocean.

Conductance calculation of a long tube with equilateral triangle cross section

Using the stratified gas flow model for calculating the conductance of long tubes with constant cross section, an analytical expression for calculating the conductance of along tube with equilateral triangle cross section has been derived. The formula given is applicable to the full pressure range. A minimum in the conductance in the intermediate flow state is shown. 2002 American vacuum Society.

Exploring the use of neural networks for biomass forecasts in the Peruvian upwelling ecosystem

A pilot study was conducted to study the ability of an artificial neural network to predict the biomass of Peruvian anchoveta Engraulis ringens, given time series of earlier biomasses, and of environmental parameters (ocenographic data and predator abundances). Acceptable predictions of three months or more appear feasible after thorough scrutiny of the input data set.

Hanseníase neural, aspectos diagnósticos da forma neural pura e mecanismos imunopatogênicos da lesão do nervo na doença. Participação de quimiocinas CCL2 e CXCL10 e metaloproteinases 2 e 9

O diagnóstico da hanseníase neural pura baseia-se em dados clínicos e laboratoriais do paciente, incluindo a histopatologia de espécimes de biópsia de nervo e detecção de DNA de Mycobacterium leprae (M. leprae) pelo PCR. Como o exame histopatológico e a técnica PCR podem não ser suficientes para confirmar o diagnóstico, a imunomarcação de lipoarabinomanana (LAM) e/ou Glicolipídio fenólico 1 (PGL1) - componentes de parede celular de M. leprae foi utilizada na primeira etapa deste estudo, na tentativa de detectar qualquer presença vestigial do M. leprae em amostras de nervo sem bacilos. Além disso, sabe-se que a lesão do nervo na hanseníase pode diretamente ser induzida pelo M. leprae nos estágios iniciais da infecção, no entanto, os mecanismos imunomediados adicionam severidade ao comprometimento da função neural em períodos sintomáticos da doença. Este estudo investigou também a expressão imuno-histoquímica de marcadores envolvidos nos mecanismos de patogenicidade do dano ao nervo na hanseníase. Os imunomarcadores selecionados foram: quimiocinas CXCL10, CCL2, CD3, CD4, CD8, CD45RA, CD45RO, CD68, HLA-DR, e metaloproteinases 2 e 9. O estudo foi desenvolvido em espécimes de biópsias congeladas de nervo coletados de pacientes com HNP (n=23 / 6 BAAR+ e 17 BAAR - PCR +) e pacientes diagnosticados com outras neuropatias (n=5) utilizados como controle. Todas as amostras foram criosseccionadas e submetidas à imunoperoxidase. Os resultados iniciais demonstraram que as 6 amostras de nervos BAAR+ são LAM+/PGL1+. Já entre as 17 amostras de nervos BAAR-, 8 são LAM+ e/ou PGL1+. Nas 17 amostras de nervos BAAR-PCR+, apenas 7 tiveram resultados LAM+ e/ou PGL1+. A detecção de imunorreatividade para LAM e PGL1 nas amostras de nervo do grupo HNP contribuiu para a maior eficiência diagnóstica na ausência recursos a diagnósticos moleculares. Os resultados da segunda parte deste estudo mostraram que foram encontradas imunoreatividade para CXCL10, CCL2, MMP2 e MMP9 nos nervos da hanseníase, mas não em amostras de nervos com outras neuropatias. Além disso, essa imunomarcação foi encontrada predominantemente em células de Schwann e em macrófagos da população celular inflamatória nos nervos HNP. Os outros marcadores de ativação imunológica foram encontrados em leucócitos (linfócitos T e macrófagos) do infiltrado inflamatório encontrados nos nervos. A expressão de todos os marcadores, exceto CXCL10, apresentou associação com a fibrose, no entanto, apenas a CCL2, independentemente dos outros imunomarcadores, estava associada a esse excessivo depósito de matriz extracelular. Nenhuma diferença na frequência da imunomarcação foi detectada entre os subgrupos BAAR+ e BAAR-, exceção feita apenas às células CD68+ e HLA-DR+, que apresentaram discreta diferença entre os grupos BAAR + e BAAR- com granuloma epitelioide. A expressão de MMP9 associada com fibrose é consistente com os resultados anteriores do grupo de pesquisa. Estes resultados indicam que as quimiocinas CCL2 e CXCL10 não são determinantes para o estabelecimento das lesões com ou sem bacilos nos em nervo em estágios avançados da doença, entretanto, a CCL2 está associada com o recrutamento de macrófagos e com o desenvolvimento da fibrose do nervo na lesão neural da hanseníase.