Combined Fluorescent-Chromogenic In Situ Hybridization for Identification and Laser Microdissection of Interphase Chromosomes

Chromosome territories constitute the most conspicuous feature of nuclear architecture, and they exhibit non-random distribution patterns in the interphase nucleus. We observed that in cell nuclei from humans with Down Syndrome two chromosomes 21 frequently localize proximal to one another and distant from the third chromosome. To systematically investigate whether the proximally positioned chromosomes were always the same in all cells, we developed an approach consisting of sequential FISH and CISH combined with laser-microdissection of chromosomes from the interphase nucleus and followed by subsequent chromosome identification by microsatellite allele genotyping. This approach identified proximally positioned chromosomes from cultured cells, and the analysis showed that the identity of the chromosomes proximally positioned varies. However, the data suggest that there may be a tendency of the same chromosomes to be positioned close to each other in the interphase nucleus of trisomic cells. The protocol described here represents a powerful new method for genome analysis

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.

Classification of neocortical interneurons using affinity propagation

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. Neuronal classification has been a difficult problem because it is unclear what a neuronal cell class actually is and what are the best characteristics are to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological or molecular characteristics, when applied to selected datasets, have provided quantitative and unbiased identification of distinct neuronal subtypes. However, better and more robust classification methods are needed for increasingly complex and larger datasets. We explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. In fact, using a combined anatomical/physiological dataset, our algorithm differentiated parvalbumin from somatostatin interneurons in 49 out of 50 cases. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.