Disentangling the attention network test: behavioral, event related potentials, and neural source analyses.

BACKGROUND The study of the attentional system remains a challenge for current neuroscience. The "Attention Network Test" (ANT) was designed to study simultaneously three different attentional networks (alerting, orienting, and executive) based in subtraction of different experimental conditions. However, some studies recommend caution with these calculations due to the interactions between the attentional networks. In particular, it is highly relevant that several interpretations about attentional impairment have arisen from these calculations in diverse pathologies. Event related potentials (ERPs) and neural source analysis can be applied to disentangle the relationships between these attentional networks not specifically shown by behavioral measures. RESULTS This study shows that there is a basic level of alerting (tonic alerting) in the no cue (NC) condition, represented by a slow negative trend in the ERP trace prior to the onset of the target stimuli. A progressive increase in the CNV amplitude related to the amount of information provided by the cue conditions is also shown. Neural source analysis reveals specific modulations of the CNV related to a task-related expectancy presented in the NC condition; a late modulation triggered by the central cue (CC) condition and probably representing a generic motor preparation; and an early and late modulation for spatial cue (SC) condition suggesting specific motor and sensory preactivation. Finally, the first component in the information processing of the target stimuli modulated by the interaction between orienting network and the executive system can be represented by N1. CONCLUSIONS The ANT is useful as a paradigm to study specific attentional mechanisms and their interactions. However, calculation of network effects is based in subtractions with non-comparable experimental conditions, as evidenced by the present data, which can induce misinterpretations in the study of the attentional capacity in human subjects.

MAR elements as tools to increase protein production by CHO cells

One of the major hurdles of isolating stable, inducible or constitutive high-level producer cell lines is the time-consuming selection, analysis and characterization of the numerous clones required to identify one with the desired characteristics. Various boundary elements, matrix attachment regions, and locus control regions were screened for for their ability to augment the expression of heterologous genes in CHO and other cells. The 5'-matrix-attachment region (MAR) of the chicken lysozyme gene was found to significantly increase stable gene expression, in culture dishes and in bioreactors. These MAR elements can be easily combined with various existing expression systems, as they can be added in trans (i.e. on a separate plasmid) in co-transfections with previously constructed expression vectors. Using cell population analysis, we found that the use of the MAR increases the proportion of high-producing CHO cell clones, thus reducing the number of cell lines that need to be screened while increasing maximal productivity. Random cDNA cloning and sequencing indicated that over 12% of the ESTs correspond to the transgene. Thus, productivity is no longer limited by transcriptional events in such MAR-containing cell lines. The identification of small and more convenient active MAR portions will also be summarized. Finally, we will show examples of how MAR elements can be combined with short term expression to increase the simultaneous synthesis of many proteins in parallel by CHO cells. Overall, we conclude that the MAR sequence is a versatile tool to increase protein expression in short and long term production processes.

Volatility smile extrapolation with an artificial neural network

I use a multi-layer feedforward perceptron, with backpropagation learning implemented via stochastic gradient descent, to extrapolate the volatility smile of Euribor derivatives over low-strikes by training the network on parametric prices.

IDED Tools and Trends, February, 2001

Newsletter for Economic Development

Tools and Trends, July 2001, Issue 16

Newsletter for Ecnonmic Development

IDED Tools and Trends, January 2002, Issue 17

Newsletter for Economic Development

IDED Tools and Trends, Summer 2002, Issue 20

Newsletter for Ecnonmic Development

IDED Tools and Trends, Fall 2002

Newsletter for Economic Development

IDED Tools and Trends, Fall 2003

Newsletter for Economic Development

IDED Tools and Trends, Summer 2003

Newsletter for Economic Development

IDED Tools and Trends, Spring 2003

Newsletter for Economic Development

Identification concept and the use of probabilities in forensic odontology--an approach by philosophical discussion.

This paper questions the practitioners' deterministic approach(es) in forensic identification and notes the limits of their conclusions in order to encourage a discussion to question current practices. With this end in view, a hypothetical discussion between an expert in dentistry and an enthusiastic member of a jury, eager to understand the scientific principles of evidence interpretation, is presented. This discussion will lead us to regard any argument aiming at identification as probabilistic.

Iowa Department of Transportation Fiscal Year 2002 Report of Savings by Using Videoconferencing through Iowa Communications Network to the Iowa General Assembly, 2002

ICN Videoconferencing by the Iowa Department of Transportation

MR connectomics: Principles and challenges.

MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches.