Free-Space Interconnect for Optical Switch and Analogue Neural Network

In this paper, the architecture of a vector-matrix multiplier (MVM) is simulated. The optical design can be made compact by the use of GRIN lenses for the optical fan-in. The intended application area was in storage area networks (SANs) but the concept can be applied to a neural network. © 2011 Allerton Press, Inc.

Desarrollo de un nuevo soporte polímerico con componentes de matriz extracelular neural para su aplicación en la diferenciación "in vitro" de distintas células primarias

268 p. : il.

Collision tumour involving a rectal gastrointestinal stromal tumour with invasion of the prostate and a prostatic adenocarcinoma

Background: Gastrointestinal stromal tumours (GISTs) are the most common primary mesenchymal neoplasia in the gastrointestinal tract, although they represent only a small fraction of total gastrointestinal malignancies in adults (<2%). GISTs can be located at any level of the gastrointestinal tract; the stomach is the most common location (60-70%), in contrast to the rectum, which is most rare (4%). When a GIST invades into the adjacent prostate tissue, it can simulate prostate cancer. In this study, we report on a case comprising the unexpected collision between a rectal GIST tumour and a prostatic adenocarcinoma. Findings: We describe the complexity of the clinical, endoscopic and radiological diagnosis, of the differential diagnosis based on tumour biopsy, and of the role of neoadjuvant therapy using imatinib prior to surgical treatment. Conclusions: Although isolated cases of coexisting GISTs and prostatic adenocarcinomas have reviously been described, this is the first reported case in the medical literature of a collision tumour involving a rectal GIST and prostatic adenocarcinoma components.

A neural extracellular matrix-based method for in vitro hippocampal neuron culture and dopaminergic differentiation of neural stem cells

Background: The ability to recreate an optimal cellular microenvironment is critical to understand neuronal behavior and functionality in vitro. An organized neural extracellular matrix (nECM) promotes neural cell adhesion, proliferation and differentiation. Here, we expanded previous observations on the ability of nECM to support in vitro neuronal differentiation, with the following goals: (i) to recreate complex neuronal networks of embryonic rat hippocampal cells, and (ii) to achieve improved levels of dopaminergic differentiation of subventricular zone (SVZ) neural progenitor cells. Methods: Hippocampal cells from E18 rat embryos were seeded on PLL- and nECM-coated substrates. Neurosphere cultures were prepared from the SVZ of P4-P7 rat pups, and differentiation of neurospheres assayed on PLL- and nECM-coated substrates. Results: When seeded on nECM-coated substrates, both hippocampal cells and SVZ progenitor cells showed neural expression patterns that were similar to their poly-L-lysine-seeded counterparts. However, nECM-based cultures of both hippocampal neurons and SVZ progenitor cells could be maintained for longer times as compared to poly-L-lysine-based cultures. As a result, nECM-based cultures gave rise to a more branched neurite arborization of hippocampal neurons. Interestingly, the prolonged differentiation time of SVZ progenitor cells in nECM allowed us to obtain a purer population of dopaminergic neurons. Conclusions: We conclude that nECM-based coating is an efficient substrate to culture neural cells at different stages of differentiation. In addition, neural ECM-coated substrates increased neuronal survival and neuronal differentiation efficiency as compared to cationic polymers such as poly-L-lysine.

Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions

This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.

Design and synthesis of biofunctional magnetic/fluorescent glyco-nanoparticles and quantum dots and their application as specific molecular imaging probes

206 p.