Prognostic value of left atrial volume in patients who underwent dobutamine stress echocardiography for known or suspected coronary artery disease

Background Left atrial volume indexed (LAVI) has been reported as a predictor of cardiovascular events. We sought to determine the prognostic value of LAVI for predicting the outcome of patients who underwent dobutamine stress echocardiography (DSE) for known or suspected coronary artery disease (CAD). Methods From January 2000 to July 2005, we studied 981 patients who underwent DSE and off-line measurements of LAVI. The value of DSE over clinical and LAVI data was examined using a stepwise log-rank test. Results During a median follow-up of 24 months, 56 (6%) events occurred. By univariate analysis, predictors of events were male sex, diabetes mellitus, previous myocardial infarction, left ventricular ejection fraction (LVEF), left atrial diameter indexed, LAVI, and abnormal DSE. By multivariate analysis, independent predictors were LVEF (relative risk [RR] = 0.98, 95% CI 0.95-1.00), LAVI (RR = 1.04, 95% CI 1.02-1.05), and abnormal DSE (RR = 2.70, 95% CI 1.28-5.69). In an incremental multivariate model, LAVI was additional to clinical data for predicting events (chi(2) 36.8, P < .001). The addition of DSE to clinical and LAVI yielded incremental information (chi(2) 55.3, P < .001). The 3-year event-free survival in patients with normal DSE and LAVI <= 33 mL/m(2) was 96%; with abnormal DSE and LAVI <= 33 mL/m(2), 91%; with normal DSE and LAVI >34 mL/m(2), 83%; and with abnormal DSE and LAVI >34 mL/m(2) 51%. Conclusion Left atrial volume indexed provides independent prognostic information in patients who underwent DSE for known or suspected CAD. Among patients with normal DSE, those with larger LAVI had worse outcome, and among patients with abnormal DSE, LAVI was still predictive. (Am Heart J 2008; 156:1110-6.)

Entropy analysis of DNA code dynamics in human chromosomes

Deoxyribonucleic acid, or DNA, is the most fundamental aspect of life but present day scientific knowledge has merely scratched the surface of the problem posed by its decoding. While experimental methods provide insightful clues, the adoption of analysis tools supported by the formalism of mathematics will lead to a systematic and solid build-up of knowledge. This paper studies human DNA from the perspective of system dynamics. By associating entropy and the Fourier transform, several global properties of the code are revealed. The fractional order characteristics emerge as a natural consequence of the information content. These properties constitute a small piece of scientific knowledge that will support further efforts towards the final aim of establishing a comprehensive theory of the phenomena involved in life.

Wavelet analysis of human DNA

This paper studies the human DNA in the perspective of signal processing. Six wavelets are tested for analyzing the information content of the human DNA. By adopting real Shannon wavelet several fundamental properties of the code are revealed. A quantitative comparison of the chromosomes and visualization through multidimensional and dendograms is developed.

Multi-dimensional scaling applied to histogram-based DNA analysis

This paper aims to study the relationships between chromosomal DNA sequences of twenty species. We propose a methodology combining DNA-based word frequency histograms, correlation methods, and an MDS technique to visualize structural information underlying chromosomes (CRs) and species. Four statistical measures are tested (Minkowski, Cosine, Pearson product-moment, and Kendall τ rank correlations) to analyze the information content of 421 nuclear CRs from twenty species. The proposed methodology is built on mathematical tools and allows the analysis and visualization of very large amounts of stream data, like DNA sequences, with almost no assumptions other than the predefined DNA “word length.” This methodology is able to produce comprehensible three-dimensional visualizations of CR clustering and related spatial and structural patterns. The results of the four test correlation scenarios show that the high-level information clusterings produced by the MDS tool are qualitatively similar, with small variations due to each correlation method characteristics, and that the clusterings are a consequence of the input data and not method’s artifacts.

Analysis of stock market indices with multidimensional scaling and wavelets

Stock market indices SMIs are important measures of financial and economical performance. Considerable research efforts during the last years demonstrated that these signals have a chaotic nature and require sophisticated mathematical tools for analyzing their characteristics. Classical methods, such as the Fourier transform, reveal considerable limitations in discriminating different periods of time. This paper studies the dynamics of SMI by combining the wavelet transform and the multidimensional scaling MDS . Six continuous wavelets are tested for analyzing the information content of the stock signals. In a first phase, the real Shannon wavelet is adopted for performing the evaluation of the SMI dynamics, while their comparison is visualized by means of the MDS. In a second phase, the other wavelets are also tested, and the corresponding MDS plots are analyzed.

A scalable and efficient approach for obtaining measurements in CAN-Based control systems

The availability of small inexpensive sensor elements enables the employment of large wired or wireless sensor networks for feeding control systems. Unfortunately, the need to transmit a large number of sensor measurements over a network negatively affects the timing parameters of the control loop. This paper presents a solution to this problem by representing sensor measurements with an approximate representation-an interpolation of sensor measurements as a function of space coordinates. A priority-based medium access control (MAC) protocol is used to select the sensor messages with high information content. Thus, the information from a large number of sensor measurements is conveyed within a few messages. This approach greatly reduces the time for obtaining a snapshot of the environment state and therefore supports the real-time requirements of feedback control loops.

Global localization with non-quantized local image features

In the field of appearance-based robot localization, the mainstream approach uses a quantized representation of local image features. An alternative strategy is the exploitation of raw feature descriptors, thus avoiding approximations due to quantization. In this work, the quantized and non-quantized representations are compared with respect to their discriminativity, in the context of the robot global localization problem. Having demonstrated the advantages of the non-quantized representation, the paper proposes mechanisms to reduce the computational burden this approach would carry, when applied in its simplest form. This reduction is achieved through a hierarchical strategy which gradually discards candidate locations and by exploring two simplifying assumptions about the training data. The potential of the non-quantized representation is exploited by resorting to the entropy-discriminativity relation. The idea behind this approach is that the non-quantized representation facilitates the assessment of the distinctiveness of features, through the entropy measure. Building on this finding, the robustness of the localization system is enhanced by modulating the importance of features according to the entropy measure. Experimental results support the effectiveness of this approach, as well as the validity of the proposed computation reduction methods.

Genetic diversity and population structure in Vicia faba L. landraces and wild related species assessed by nuclear SSRs

Research Article

The missing link in key sectors analysis

In general terms key sectors analysis aims at identifying the role, or impact, that the existence of a productive sector has in the economy. Quite a few measures, indicators and methodologies of varied complexity have been proposed in the literature, from multiplier sums to extraction methods, but not without debate about their properties and their information content. All of them, to our knowledge, focus exclusively on the interdependence effects that result from the input-output structure of the economy. By so doing the simple input-output approach misses critical links beyond the interindustry ones. A productive sector’s role is that of producing but also that of generating and distributing income among primary factors as a result of production. Thus when measuring a sector’s role, the income generating process cannot and should not be omitted if we want to better elucidate the sector’ economic role. A simple way to make the missing income link explicit is to use the SAM (Soci

Human capital in growth regressions: how much difference does data quality make? An update and further results

We construct estimates of educational attainment for a sample of OECD countries using previously unexploited sources. We follow a heuristic approach to obtain plausible time profiles for attainment levels by removing sharp breaks in the data that seem to reflect changes in classification criteria. We then construct indicators of the information content of our series and a number of previously available data sets and examine their performance in several growth specifications. We find a clear positive correlation between data quality and the size and significance of human capital coefficients in growth regressions. Using an extension of the classical errors in variables model, we construct a set of meta-estimates of the coefficient of years of schooling in an aggregate Cobb-Douglas production function. Our results suggest that, after correcting for measurement error bias, the value of this parameter is well above 0.50.

Automated quantitative histology reveals vascular morphodynamics during Arabidopsis hypocotyl secondary growth.

Among various advantages, their small size makes model organisms preferred subjects of investigation. Yet, even in model systems detailed analysis of numerous developmental processes at cellular level is severely hampered by their scale. For instance, secondary growth of Arabidopsis hypocotyls creates a radial pattern of highly specialized tissues that comprises several thousand cells starting from a few dozen. This dynamic process is difficult to follow because of its scale and because it can only be investigated invasively, precluding comprehensive understanding of the cell proliferation, differentiation, and patterning events involved. To overcome such limitation, we established an automated quantitative histology approach. We acquired hypocotyl cross-sections from tiled high-resolution images and extracted their information content using custom high-throughput image processing and segmentation. Coupled with automated cell type recognition through machine learning, we could establish a cellular resolution atlas that reveals vascular morphodynamics during secondary growth, for example equidistant phloem pole formation. DOI: http://dx.doi.org/10.7554/eLife.01567.001.

Probabilistic base calling of Solexa sequencing data.

BACKGROUND: Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges; currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology. RESULTS: We propose a novel base calling algorithm using model-based clustering and probability theory to identify ambiguous bases and code them with IUPAC symbols. We also select optimal sub-tags using a score based on information content to remove uncertain bases towards the ends of the reads. CONCLUSION: We show that the method improves genome coverage and number of usable tags as compared with Solexa's data processing pipeline by an average of 15%. An R package is provided which allows fast and accurate base calling of Solexa's fluorescence intensity files and the production of informative diagnostic plots.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

The adaptative function of melanin-based colour variants

Résumé : Les mécanismes de contrôle des couleurs mélaniques chez les vertébrés sont encore discutés parmi les biologistes de l'évolution. Une hypothèse récente affirme que les effets pléiotropies du système des mélanocortines expliquent l'association fréquente entre la coloration eumélanique noire (due à la déposition d'eumélanine) et de nombreux traits physiologiques et comportementaux. De nombreuses études suggèrent, en effet, que des niveaux plus élevés des mélanocortines induisent l'assombrissement des téguments eumélaniques et affectent d'autres traits phénotypiques simultanément. Cependant, il n'est pas encore établi si ce mécanisme de pléiotropie peut s'appliquer aux colorations dues à la déposition de phaeomélanine, une autre forme commune de mélanine. Les antagonistes des mélanocortines déclenchent le phaeomélanogenèse et bloquent l'effet des mélanocortines ou ont un effet pharmacologique opposé. Nous nous proposons donc d'évaluer l'hypothèse que les effets pléiotropes des antagonistes des mélanocortines génèrent des covariations entre la coloration phaeomélanique et des aspects de la qualité individuelle. Comme prédit par cette hypothèse, nous constatons chez la chouette effraie (Tyto alba) que les traits phénotypiques (résistance au stress oxydatif et aux parasites) corrèlent positivement au degré d'expression d'une couleur eumélanique mais négativement au degré d'expression d'une coloration phaeomélanique. Puis, nous montrons chez la chouette hulotte (Strix aluco) que les associations génétiques entre la coloration phaeomélanique et la physiologie (immunité et la régulation de l'homéostasie) confèrent des avantages aux individus de différentes couleurs dans différents environnements caractérisés par l'abondance de nourriture et le niveau d'exposition aux parasites. Ainsi, nos études soutiennent l'hypothèse que les effets pléiotropes des antagonistes des mélanocortines génèrent des covariations entre les traits mélaniques et divers aspects de la qualité individuelle. Finalement, nous montrons chez le faucon crécerelle (Falco Tinnunculus) que l'expression des ornements mélaniques est sensible à la qualité de l'environnement dans lequel les individus grandissent. Ceci suggère que les gènes codant pour les mélanocortines et leurs antagonistes pourraient induire une expression des traits mélaniques dépendante de la condition de l'individu, un pattern d'expression rarement observé pour des traits généralement sous fort contrôle génétique. Summary : The information content and control mechanisms of melanin-based colour signals in vertebrates are still debated among evolutionary biologists. A recent hypothesis contends that pleiotropic effects of the melanocortin system accounts for the frequent association between black eumelanic coloration and physiological and behavioural traits. Accordingly, empirical evidence suggests that higher levels of melanocortins concurrently promote darker eumelanic integuments and affect other phenotypic traits. However, whether this mechanism may apply to signals relying on phaeomelanin, another common form of melanin pigments, remains to be established. Melanocortin antagonists trigger phaeomelanogenesis and block the effect of melanocortins or result in the opposite pharmacological effect. Therefore, we tested the hypothesis that pleiotropic effects of melanocortin antagonists and inverse agonists account for covariations between phaeomelanin-based coloration and aspects of individual quality. As predicted, we found that phenotypic traits (resistance to oxidative stress and parasites) correlated positively with a eumelanic trait and negatively with a phaeomelanic trait in the barn owl (Tyto alba). Then, we showed in the tawny owl (Strix aluco) that genetic associations between phaeomelanin-based coloration and physiology (immunity and regulation of energy homeostasis) confer benefits to differently coloured individuals under different levels of food abundance and parasite exposure. Altogether, our studies support the hypothesis that pleiotropic effects of melanocortins antagonists can indeed account for covariations between phaeomelanin-based traits and aspects of individual quality. Eventually, we show in the Eurasian kestrel (Falco Tinnunculus) that expression of melanin-based ornaments is sensitive to the environment in which individuals grow. This suggests that genes coding for melanocortins and their antagonists can mediate the condition-dependent component of melanin-based traits.

Monetary policy with signal extraction from the bond market

Monetary policy is conducted in an environment of uncertainty. This paper sets upa model where the central bank uses real-time data from the bond market togetherwith standard macroeconomic indicators to estimate the current state of theeconomy more efficiently, while taking into account that its own actions influencewhat it observes. The timeliness of bond market data allows for quicker responsesof monetary policy to disturbances compared to the case when the central bankhas to rely solely on collected aggregate data. The information content of theterm structure creates a link between the bond market and the macroeconomythat is novel to the literature. To quantify the importance of the bond market asa source of information, the model is estimated on data for the United Statesand Australia using Bayesian methods. The empirical exercise suggests that thereis some information in the US term structure that helps the Federal Reserve toidentify shocks to the economy on a timely basis. Australian bond prices seemto be less informative than their US counterparts, perhaps because Australia is arelatively small and open economy.