Indicators used to measure the innovation process: defects and possible remedies

Some diverse indicators used to measure the innovation process are considered, They include those with art aggregate, and often national, focus, and rely on data from scientific publications, patents and R&D expenditures, etc. Others have a firm-level perspective, relying primarily on surveys or case studies. Also included are indicators derived from specialized databases, or consensual agreements reached through foresight exercises. There is an obvious need for greater integration of the various approaches to capture move effectively the richness of available data and better reflect the reality of innovation. The focus for such integration could be in the area of technology strategy, which integrates the diverse scientific, technological, and innovation activities of firms within their operating environments; improved capacity to measure it has implications for policy-makers, managers and researchers.

Context-free and context-sensitive dynamics in recurrent neural networks

Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languages. The dynamics of such networks is usually based on damped oscillation around fixed points in state space and requires that the dynamical components are arranged in certain ways. It is shown that qualitatively similar dynamics with similar constraints hold for a(n)b(n)c(n), a context-sensitive language. The additional difficulty with a(n)b(n)c(n), compared with the context-free language a(n)b(n), consists of 'counting up' and 'counting down' letters simultaneously. The network solution is to oscillate in two principal dimensions, one for counting up and one for counting down. This study focuses on the dynamics employed by the sequential cascaded network, in contrast to the simple recurrent network, and the use of backpropagation through time. Found solutions generalize well beyond training data, however, learning is not reliable. The contribution of this study lies in demonstrating how the dynamics in recurrent neural networks that process context-free languages can also be employed in processing some context-sensitive languages (traditionally thought of as requiring additional computation resources). This continuity of mechanism between language classes contributes to our understanding of neural networks in modelling language learning and processing.

Identifying rate-limiting nodes in large-scale cortical networks for visuospatial processing: an illustration using fMRI

With the advent of functional neuroimaging techniques, in particular functional magnetic resonance imaging (fMRI), we have gained greater insight into the neural correlates of visuospatial function. However, it may not always be easy to identify the cerebral regions most specifically associated with performance on a given task. One approach is to examine the quantitative relationships between regional activation and behavioral performance measures. In the present study, we investigated the functional neuroanatomy of two different visuospatial processing tasks, judgement of line orientation and mental rotation. Twenty-four normal participants were scanned with fMRI using blocked periodic designs for experimental task presentation. Accuracy and reaction time (RT) to each trial of both activation and baseline conditions in each experiment was recorded. Both experiments activated dorsal and ventral visual cortical areas as well as dorsolateral prefrontal cortex. More regionally specific associations with task performance were identified by estimating the association between (sinusoidal) power of functional response and mean RT to the activation condition; a permutation test based on spatial statistics was used for inference. There was significant behavioral-physiological association in right ventral extrastriate cortex for the line orientation task and in bilateral (predominantly right) superior parietal lobule for the mental rotation task. Comparable associations were not found between power of response and RT to the baseline conditions of the tasks. These data suggest that one region in a neurocognitive network may be most strongly associated with behavioral performance and this may be regarded as the computationally least efficient or rate-limiting node of the network.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Synthesis and Characterization of Semi-Interpenetrating Networks Based on Poly(dimethylsiloxane) and Poly(vinyl alcohol)

Semi-interpenetrating networks (Semi-IPNs) with different compositions were prepared from poly(dimethylsiloxane) (PDMS), tetraethylorthosilicate (TEOS), and poly (vinyl alcohol) (PVA) by the sol-gel process in this study. The characterization of the PDMS/PVA semi-IPN was carried out using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and swelling measurements. The presence of PVA domains dispersed in the PDMS network disrupted the network and allowed PDMS to crystallize, as observed by the crystallization and melting peaks in the DSC analyses. Because of the presence of hydrophilic (-OH) and hydrophobic (Si-(CH(3))(2)) domains, there was an appropriate hydrophylic/hydrophobic balance in the semi-IPNs prepared, which led to a maximum equilibrium water content of similar to 14 wt % without a loss in the ability to swell less polar solvents. (C) 2009 Wiley Periodicals, Inc. J Appl Polym Sci 115: 158-166, 2010

Visuospatial processing and the function of prefrontal-parietal networks in autism spectrum disorders: a functional MRI study

Objective: Individuals with autism spectrum disorders typically have normal visuospatial abilities but impaired executive functioning, particularly in abilities related to working memory and attention. The aim of this study was to elucidate the functioning of frontoparietal networks underlying spatial working memory processes during mental rotation in persons with autism spectrum disorders. Method: Seven adolescent males with normal IQ with an autism spectrum disorder and nine age- and IQ-matched male comparison subjects underwent functional magnetic resonance imaging scans while performing a mental rotation task. Results: The autism spectrum disorders group showed less activation in lateral and medial premotor cortex, dorsolateral prefrontal cortex, anterior cingulate gyrus, and caudate nucleus. Conclusions: The finding of less activation in prefrontal regions but not in parietal regions supports a model of dysfunction of frontostriatal networks in autism spectrum disorders.

An fMRI Normative Database for Connectivity Networks Using One-Class Support Vector Machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

The GHEP-EMPOP collaboration on mtDNA population data-A new resource for forensic casework

Mitochondrial DNA (mtDNA) population data for forensic purposes are still scarce for some populations, which may limit the evaluation of forensic evidence especially when the rarity of a haplotype needs to be determined in a database search. In order to improve the collection of mtDNA lineages from the Iberian and South American subcontinents, we here report the results of a collaborative study involving nine laboratories from the Spanish and Portuguese Speaking Working Group of the International Society for Forensic Genetics (GHEP-ISFG) and EMPOP. The individual laboratories contributed population data that were generated throughout the past 10 years, but in the majority of cases have not been made available to the scientific community. A total of 1019 haplotypes from Iberia (Basque Country, 2 general Spanish populations, 2 North and 1 Central Portugal populations), and Latin America (3 populations from Sao Paulo) were collected, reviewed and harmonized according to defined EMPOP criteria. The majority of data ambiguities that were found during the reviewing process (41 in total) were transcription errors confirming that the documentation process is still the most error-prone stage in reporting mtDNA population data, especially when performed manually. This GHEP-EMPOP collaboration has significantly improved the quality of the individual mtDNA datasets and adds mtDNA population data as valuable resource to the EMPOP database (www.empop.org). (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Salvage Radical Prostatectomy for Radiation-recurrent Prostate Cancer: A Multi-institutional Collaboration

Background: Oncologic outcomes in men with radiation-recurrent prostate cancer (PCa) treated with salvage radical prostatectomy (SRP) are poorly defined. Objective: To identify predictors of biochemical recurrence (BCR), metastasis, and death following SRP to help select patients who may benefit from SRP. Design, setting, and participants: This is a retrospective, international, multi-institutional cohort analysis. There was amedian follow-up of 4.4 yr following SRP performed on 404 men with radiation-recurrent PCa from 1985 to 2009 in tertiary centers. Intervention: Open SRP. Measurements: BCR after SRP was defined as a serum prostate-specific antigen (PSA) >= 0.1 or >= 0.2 ng/ml (depending on the institution). Secondary end points included progression to metastasis and cancerspecific death. Results and limitations: Median age at SRP was 65 yr of age, and median pre-SRP PSA was 4.5 ng/ml. Following SRP, 195 patients experienced BCR, 64 developed metastases, and 40 died from PCa. At 10 yr after SRP, BCR-free survival, metastasis-free survival, and cancer-specific survival (CSS) probabilities were 37% (95% confidence interval [CI], 31-43), 77% (95% CI, 71-82), and 83% (95% CI, 76-88), respectively. On preoperative multivariable analysis, pre-SRP PSA and Gleason score at postradiation prostate biopsy predicted BCR (p = 0.022; global p < 0.001) and metastasis (p = 0.022; global p < 0.001). On postoperative multivariable analysis, pre-SRP PSA and pathologic Gleason score at SRP predicted BCR (p = 0.014; global p < 0.001) and metastasis (p < 0.001; global p < 0.001). Lymph node involvement (LNI) also predicted metastasis (p = 0.017). The main limitations of this study are its retrospective design and the follow-up period. Conclusions: In a select group of patients who underwent SRP for radiation-recurrent PCa, freedom from clinical metastasis was observed in > 75% of patients 10 yr after surgery. Patients with lower pre-SRP PSA levels and lower postradiation prostate biopsy Gleason score have the highest probability of cure from SRP. (C) 2011 European Association of Urology. Published by Elsevier B. V. All rights reserved.