Fast and Efficient Neural Conversion of Human Hematopoietic Cells

Neurons obtained directly from human somatic cells hold great promise for disease modeling and drug screening. Available protocols rely on overexpression of transcription factors using integrative vectors and are often slow, complex, and inefficient. We report a fast and efficient approach for generating induced neural cells (iNCs) directly from human hematopoietic cells using Sendai virus. Upon SOX2 and c-MYC expression, CD133-positive cord blood cells rapidly adopt a neuroepithelial morphology and exhibit high expansion capacity. Under defined neurogenic culture conditions, they express mature neuronal markers and fire spontaneous action potentials that can be modulated with neurotransmitters. SOX2 and c-MYC are also sufficient to convert peripheral blood mononuclear cells into iNCs. However, the conversion process is less efficient and resulting iNCs have limited expansion capacity and electrophysiological activity upon differentiation. Our study demonstrates rapid and efficient generation of iNCs from hematopoietic cells while underscoring the impact of target cells on conversion efficiency.

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.

Defeitos do tubo neural causam alterações estruturais no ureter? Estudo em fetos humanos

Anencefalia é o defeito do tubo neural mais severo. A morfologia do ureter de fetos anencéfalos é desconhecida. O objetivo deste trabalho é analisar a estrutura do ureter de fetos humanos normais e anencéfalos (FHA). Nós estudamos 16 ureteres de 8 fetos sem anomalias congênitas (4 masculinos e 4 femininos) com idades entre 16 e 27 semanas pós concepção (SPC) e 14 ureteres de 7 FHA (4 masculinos e 3 femininos) com idades entre 19 e 33 SPC. Os ureteres foram dissecados e emblocados em parafina. Foram feitos cortes com 5 m e depois corados com Tricrômico de Masson, para quantificação das células de músculo liso (CML) e determinação da área da a luz do ureter, espessura e diâmetro. As amostras também foram coradas com Resorcina Fucsina de Weigert ( para observação das fibras elásticas) e Vermelho de Picro Sirius com polarização e análise imunohistoquímica das fibras do colágeno tipo III. Os dados da quantificação do músculo foram expressos em densidade volumétrica (Vv-%). As imagens foram capturadas com microscópio Olympus BX51 e câmera Olympus DP70. A análise morfológica da área do lúmen, espessura e diâmetro foram feitas usando o software Image J. As médias foram comparadas usando o teste t não pareado (p<0.05). O epitélio do ureter estava bem preservado em ambos os grupos, e não houve diferença entre os grupos. Não observamos fibras do sistema elástico em qualquer ureter analisados. Concentração de músculo liso (Vv) não diferiram significativamente (p = 0,4413) em FHA (12% 1,628) e grupo controle (13,51% 0,9231). A área de luz ureteral foi significativamente menor (p = 0,0341) em FHA (6365μm 1,282), quando comparado ao grupo controle (20,170 5,480 mM). O diâmetro ureteral foi significativamente menor (p = 0,0294) em FHA (166.7μm 10,99) quando comparado ao grupo controle (240 26,6 mM). A espessura ureteral foi significativamente menor (p = 0,0448) em FHA (30.57μm 2,034), quando comparado ao grupo controle (7,453 47.49μm). Colágeno tipo III foi observado em maior quantidade nos ureteres da FHA. Alterações estruturais ureterais nos fetos anencéfalos foram significativas em nosso estudo. O ureter de fetos com anencefalia mostraram mais concentração de colágeno tipo III, menor diâmetro, área e espessura. Nervos ureterais em FHA podem ser modificados devido a lesão cerebral com consequente dano no controle dos nervos ureterais. Isto pode levar a alterações estruturais no ureter de fetos anencéfalos.

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.