A feature selection approach for identification of signature genes from SAGE data

Abstract Background One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements. Results A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology. Conclusion The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS) or the recent Sequencing-By-Synthesis (SBS) technique. Some of such genes identified by the proposed method may be useful to generate classifiers.

Emergence of sensory selection mechanisms in Artificial Life simulations

Background The evolutionary advantages of selective attention are unclear. Since the study of selective attention began, it has been suggested that the nervous system only processes the most relevant stimuli because of its limited capacity [1]. An alternative proposal is that action planning requires the inhibition of irrelevant stimuli, which forces the nervous system to limit its processing [2]. An evolutionary approach might provide additional clues to clarify the role of selective attention. Methods We developed Artificial Life simulations wherein animals were repeatedly presented two objects, "left" and "right", each of which could be "food" or "non-food." The animals' neural networks (multilayer perceptrons) had two input nodes, one for each object, and two output nodes to determine if the animal ate each of the objects. The neural networks also had a variable number of hidden nodes, which determined whether or not it had enough capacity to process both stimuli (Table 1). The evolutionary relevance of the left and the right food objects could also vary depending on how much the animal's fitness was increased when ingesting them (Table 1). We compared sensory processing in animals with or without limited capacity, which evolved in simulations in which the objects had the same or different relevances. Table 1. Nine sets of simulations were performed, varying the values of food objects and the number of hidden nodes in the neural networks. The values of left and right food were swapped during the second half of the simulations. Non-food objects were always worth -3. The evolution of neural networks was simulated by a simple genetic algorithm. Fitness was a function of the number of food and non-food objects each animal ate and the chromosomes determined the node biases and synaptic weights. During each simulation, 10 populations of 20 individuals each evolved in parallel for 20,000 generations, then the relevance of food objects was swapped and the simulation was run again for another 20,000 generations. The neural networks were evaluated by their ability to identify the two objects correctly. The detectability (d') for the left and the right objects was calculated using Signal Detection Theory [3]. Results and conclusion When both stimuli were equally relevant, networks with two hidden nodes only processed one stimulus and ignored the other. With four or eight hidden nodes, they could correctly identify both stimuli. When the stimuli had different relevances, the d' for the most relevant stimulus was higher than the d' for the least relevant stimulus, even when the networks had four or eight hidden nodes. We conclude that selection mechanisms arose in our simulations depending not only on the size of the neuron networks but also on the stimuli's relevance for action.

Parallel amino acid replacements in the rhodopsins of the rockfishes (Sebastes spp.) associated with shifts in habitat depth

Among various groups of fishes, a shift in peak wavelength sensitivity has been correlated with changes in their photic environments. The genus Sebastes is a radiation of marine fish species that inhabit a wide range of depths from intertidal to over 600 m. We examined 32 species of Sebastes for evidence of adaptive amino acid substitution at the rhodopsin gene. Fourteen amino acid positions were variable among these species. Maximum likelihood analyses identify several of these to be targets of positive selection. None of these correspond to previously identified critical amino acid sites, yet they may in fact be functionally important. The occurrence of independent parallel changes at certain amino acid positions reinforces this idea. Reconstruction of habitat depths of ancestral nodes in the phylogeny suggests that shallow habitats have been colonized independently in different lineages. The evolution of rhodopsin appears to be associated with changes in depth, with accelerated evolution in lineages that have had large changes in depth.

Parallel divergent adaptation along replicated altitudinal gradients in Alpine trout

Background The European trout (Salmo trutta species complex) occurs across a very wide altitudinal range from lowland rivers to alpine streams. Historically, the major European river systems contained different, evolutionarily distinct trout lineages, and some of this genetic diversity has persisted in spite of extensive human-mediated translocations. We used AFLP-based genome scans to investigate the extent of potentially adaptive divergence among major drainages and along altitudinal gradients replicated in several rivers. Results The proportion of loci showing evidence of divergent selection was larger between drainages than along altitudinal transects within drainages. This suggests divergent selection is stronger between drainages, or adaptive divergence is constrained by gene flow among populations within drainages, although the latter could not be confirmed at a more local scale. Still, altitudinal divergence occurred and, at approximately 2% of the markers, parallel changes of the AFLP band frequencies with altitude were observed suggesting that altitude may well be an important source of divergent selection within rivers. Conclusions Our results indicate that adaptive genetic divergence is common both between major European river systems and along altitudinal gradients within drainages. Alpine trout appear to be a promising model system to investigate the relative roles of divergent selection and gene flow in promoting or preventing adaptation to climate gradients.

Parallel evolution of facial stripe patterns in the Neolamprologus brichardilpulcher species complex endemic to Lake Tanganyika

Colour pattern diversity can be due to random processes or to natural or sexual selection. Consequently, similarities in colour patterns are not always correlated with common ancestry, but may result from convergent evolution under shared selection pressures or drift. Neolamprologus brichardi and Neolamprologus pulcher have been described as two distinct species based on differences in the arrangement of two dark bars on the operculum. Our study uses DNA sequences of the mitochondrial control region to show that relatedness of haplotypes disagrees with species assignment based on head colour pattern. This suggests repeated parallel evolution of particular stripe patterns. The complete lack of shared haplotypes between populations of the same or different phenotypes reflects strong philopatric behaviour, possibly induced by the cooperative breeding mode in which offspring remain in their natal territory and serve as helpers until they disperse to nearby territories or take over a breeding position. Concordant phylogeographic patterns between N. brichardi/N. pulcher populations and other rock-dwelling cichlids suggest that the same colonization routes have been taken by sympatric species and that these routes were affected by lake level fluctuations in the past. (C) 2007 Elsevier Inc. All rights reserved.

Genomic signatures of relaxed disruptive selection associated with speciation reversal in whitefish

Background: Speciation reversal: the erosion of species differentiation via an increase in introgressive hybridization due to the weakening of previously divergent selection regimes, is thought to be an important, yet poorly understood, driver of biodiversity loss. Our study system, the Alpine whitefish (Coregonus spp.) species complex is a classic example of a recent postglacial adaptive radiation: forming an array of endemic lake flocks, with the independent origination of similar ecotypes among flocks. However, many of the lakes of the Alpine radiation have been seriously impacted by anthropogenic nutrient enrichment, resulting in a collapse in neutral genetic and phenotypic differentiation within the most polluted lakes. Here we investigate the effects of eutrophication on the selective forces that have shaped this radiation, using population genomics. We studied eight sympatric species assemblages belonging to five independent parallel adaptive radiations, and one species pair in secondary contact. We used AFLP markers, and applied FST outlier (BAYESCAN, DFDIST) and logistic regression analyses (MATSAM), to identify candidate regions for disruptive selection in the genome and their associations with adaptive traits within each lake flock. The number of outlier and adaptive trait associated loci identified per lake were then regressed against two variables (historical phosphorus concentration and contemporary oxygen concentration) representing the strength of eutrophication. Results: Whilst we identify disruptive selection candidate regions in all lake flocks, we find similar trends, across analysis methods, towards fewer disruptive selection candidate regions and fewer adaptive trait/candidate loci associations in the more polluted lakes. Conclusions: Weakened disruptive selection and a concomitant breakdown in reproductive isolating mechanisms in more polluted lakes has lead to increased gene flow between coexisting Alpine whitefish species. We hypothesize that the resulting higher rates of interspecific recombination reduce either the number or extent of genomic islands of divergence surrounding loci evolving under disruptive natural selection. This produces the negative trend seen in the number of selection candidate loci recovered during genome scans of whitefish species flocks, with increasing levels of anthropogenic eutrophication: as the likelihood decreases that AFLP restriction sites will fall within regions of heightened genomic divergence and therefore be classified as FST outlier loci. This study explores for the first time the potential effects of human-mediated relaxation of disruptive selection on heterogeneous genomic divergence between coexisting species.

Adaptive plasticity and genetic divergence in feeding efficiency during parallel adaptive radiation of whitefish (Coregonus spp.)

Parallel phenotypic divergence in replicated adaptive radiations could either result from parallel genetic divergence in response to similar divergent selec- tion regimes or from equivalent phenotypically plastic response to the repeated occurrence of contrasting environments. In post-glacial fish, repli- cated divergence in phenotypes along the benthic-limnetic habitat axis is commonly observed. Here, we use two benthic-limnetic species pairs of whitefish from two Swiss lakes, raised in a common garden design, with reciprocal food treatments in one species pair, to experimentally measure whether feeding efficiency on benthic prey has a genetic basis or whether it underlies phenotypic plasticity (or both). To do so, we offered experimental fish mosquito larvae, partially burried in sand, and measured multiple feed- ing efficiency variables. Our results reveal both, genetic divergence as well as phenotypically plastic divergence in feeding efficiency, with the pheno- typically benthic species raised on benthic food being the most efficient forager on benthic prey. This indicates that both, divergent natural selection on genetically heritable traits and adaptive phenotypic plasticity, are likely important mechanisms driving phenotypic divergence in adaptive radiation.

Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set

Stepwise uncertainty reduction (SUR) strategies aim at constructing a sequence of points for evaluating a function  f in such a way that the residual uncertainty about a quantity of interest progressively decreases to zero. Using such strategies in the framework of Gaussian process modeling has been shown to be efficient for estimating the volume of excursion of f above a fixed threshold. However, SUR strategies remain cumbersome to use in practice because of their high computational complexity, and the fact that they deliver a single point at each iteration. In this article we introduce several multipoint sampling criteria, allowing the selection of batches of points at which f can be evaluated in parallel. Such criteria are of particular interest when f is costly to evaluate and several CPUs are simultaneously available. We also manage to drastically reduce the computational cost of these strategies through the use of closed form formulas. We illustrate their performances in various numerical experiments, including a nuclear safety test case. Basic notions about kriging, auxiliary problems, complexity calculations, R code, and data are available online as supplementary materials.

Total Tumor Volume and Alpha Fetoprotein for selection of transplant candidates with hepatocellular carcinoma: A prospective validation.

The selection of liver transplant candidates with hepatocellular carcinoma (HCC) is currently validated based on Milan criteria. The use of extended criteria has remained a matter of debate, mainly because of the absence of prospective validation. The present prospective study recruited patients according to the previously proposed Total Tumor Volume (TTV ≤115 cm(3) )/alpha fetoprotein (AFP ≤400 ng/ml) score. Patients with AFP >400 ng/ml were excluded, and as such the Milan group was modified to include only patients with AFP <400 ng/ml; these patients were compared to patients beyond Milan, but within TTV/AFP. From January 2007 to March 2013, 233 patients with HCC were listed for liver transplantation. Of them, 195 patients were within Milan, and 38 beyond Milan but within TTV/AFP. The average follow-up from listing was 33,9 ±24,9 months. The risk of drop-out was higher for patients beyond Milan but within TTV/AFP (16/38, 42,1%), than for patients within Milan (49/195, 25,1%, p=0,033). In parallel, intent-to-treat survival from listing was lower in the patients beyond Milan (53,8% vs. 71,6% at four years, p<0,001). After a median waiting time of 8 months, 166 patients were transplanted, 134 patients within Milan criteria, and 32 beyond Milan but within TTV/AFP. They demonstrated acceptable and similar recurrence rates (4,5% vs. 9,4%, p=0,138) and post-transplant survivals (78,7% vs. 74,6% at four years, p=0,932). CONCLUSION Based on the present prospective study, HCC liver transplant candidate selection could be expanded to the TTV (≤115 cm(3) )/AFP (≤400 ng/ml) criteria in centers with at least 8-month waiting time. An increased risk of drop-out on the waiting list can be expected but with equivalent and satisfactory post-transplant survival. This article is protected by copyright. All rights reserved.

Divided we stand: Parallel distributed stack memory management

We present an overview of the stack-based memory management techniques that we used in our non-deterministic and-parallel Prolog systems: &-Prolog and DASWAM. We believe that the problems associated with non-deterministic and-parallel systems are more general than those encountered in or-parallel and deterministic and-parallel systems, which can be seen as subsets of this more general case. We develop on the previously proposed "marker scheme", lifting some of the restrictions associated with the selection of goals while keeping (virtual) memory consumption down. We also review some of the other problems associated with the stack-based management scheme, such as handling of forward and backward execution, cut, and roll-backs.

Parallel Computer Vision Algorithms for Graphics Processing Units

La evolución de los teléfonos móviles inteligentes, dotados de cámaras digitales, está provocando una creciente demanda de aplicaciones cada vez más complejas que necesitan algoritmos de visión artificial en tiempo real; puesto que el tamaño de las señales de vídeo no hace sino aumentar y en cambio el rendimiento de los procesadores de un solo núcleo se ha estancado, los nuevos algoritmos que se diseñen para visión artificial han de ser paralelos para poder ejecutarse en múltiples procesadores y ser computacionalmente escalables. Una de las clases de procesadores más interesantes en la actualidad se encuentra en las tarjetas gráficas (GPU), que son dispositivos que ofrecen un alto grado de paralelismo, un excelente rendimiento numérico y una creciente versatilidad, lo que los hace interesantes para llevar a cabo computación científica. En esta tesis se exploran dos aplicaciones de visión artificial que revisten una gran complejidad computacional y no pueden ser ejecutadas en tiempo real empleando procesadores tradicionales. En cambio, como se demuestra en esta tesis, la paralelización de las distintas subtareas y su implementación sobre una GPU arrojan los resultados deseados de ejecución con tasas de refresco interactivas. Asimismo, se propone una técnica para la evaluación rápida de funciones de complejidad arbitraria especialmente indicada para su uso en una GPU. En primer lugar se estudia la aplicación de técnicas de síntesis de imágenes virtuales a partir de únicamente dos cámaras lejanas y no paralelas—en contraste con la configuración habitual en TV 3D de cámaras cercanas y paralelas—con información de color y profundidad. Empleando filtros de mediana modificados para la elaboración de un mapa de profundidad virtual y proyecciones inversas, se comprueba que estas técnicas son adecuadas para una libre elección del punto de vista. Además, se demuestra que la codificación de la información de profundidad con respecto a un sistema de referencia global es sumamente perjudicial y debería ser evitada. Por otro lado se propone un sistema de detección de objetos móviles basado en técnicas de estimación de densidad con funciones locales. Este tipo de técnicas es muy adecuada para el modelado de escenas complejas con fondos multimodales, pero ha recibido poco uso debido a su gran complejidad computacional. El sistema propuesto, implementado en tiempo real sobre una GPU, incluye propuestas para la estimación dinámica de los anchos de banda de las funciones locales, actualización selectiva del modelo de fondo, actualización de la posición de las muestras de referencia del modelo de primer plano empleando un filtro de partículas multirregión y selección automática de regiones de interés para reducir el coste computacional. Los resultados, evaluados sobre diversas bases de datos y comparados con otros algoritmos del estado del arte, demuestran la gran versatilidad y calidad de la propuesta. Finalmente se propone un método para la aproximación de funciones arbitrarias empleando funciones continuas lineales a tramos, especialmente indicada para su implementación en una GPU mediante el uso de las unidades de filtraje de texturas, normalmente no utilizadas para cómputo numérico. La propuesta incluye un riguroso análisis matemático del error cometido en la aproximación en función del número de muestras empleadas, así como un método para la obtención de una partición cuasióptima del dominio de la función para minimizar el error. ABSTRACT The evolution of smartphones, all equipped with digital cameras, is driving a growing demand for ever more complex applications that need to rely on real-time computer vision algorithms. However, video signals are only increasing in size, whereas the performance of single-core processors has somewhat stagnated in the past few years. Consequently, new computer vision algorithms will need to be parallel to run on multiple processors and be computationally scalable. One of the most promising classes of processors nowadays can be found in graphics processing units (GPU). These are devices offering a high parallelism degree, excellent numerical performance and increasing versatility, which makes them interesting to run scientific computations. In this thesis, we explore two computer vision applications with a high computational complexity that precludes them from running in real time on traditional uniprocessors. However, we show that by parallelizing subtasks and implementing them on a GPU, both applications attain their goals of running at interactive frame rates. In addition, we propose a technique for fast evaluation of arbitrarily complex functions, specially designed for GPU implementation. First, we explore the application of depth-image–based rendering techniques to the unusual configuration of two convergent, wide baseline cameras, in contrast to the usual configuration used in 3D TV, which are narrow baseline, parallel cameras. By using a backward mapping approach with a depth inpainting scheme based on median filters, we show that these techniques are adequate for free viewpoint video applications. In addition, we show that referring depth information to a global reference system is ill-advised and should be avoided. Then, we propose a background subtraction system based on kernel density estimation techniques. These techniques are very adequate for modelling complex scenes featuring multimodal backgrounds, but have not been so popular due to their huge computational and memory complexity. The proposed system, implemented in real time on a GPU, features novel proposals for dynamic kernel bandwidth estimation for the background model, selective update of the background model, update of the position of reference samples of the foreground model using a multi-region particle filter, and automatic selection of regions of interest to reduce computational cost. The results, evaluated on several databases and compared to other state-of-the-art algorithms, demonstrate the high quality and versatility of our proposal. Finally, we propose a general method for the approximation of arbitrarily complex functions using continuous piecewise linear functions, specially formulated for GPU implementation by leveraging their texture filtering units, normally unused for numerical computation. Our proposal features a rigorous mathematical analysis of the approximation error in function of the number of samples, as well as a method to obtain a suboptimal partition of the domain of the function to minimize approximation error.

Polyploid formation created unique avenues for response to selection in Gossypium (cotton)

A detailed restriction fragment length polymorphism map was used to determine the chromosomal locations and subgenomic distributions of quantitative trait loci (QTLs) segregating in a cross between cultivars of allotetraploid (AADD) Gossypium hirsutum (“Upland” cotton) and Gossypium barbadense (“Sea Island,” “Pima,” or “Egyptian” cotton) that differ markedly in the quality and quantity of seed epidermal fibers. Most QTLs influencing fiber quality and yield are located on the “D” subgenome, derived from an ancestor that does not produce spinnable fibers. D subgenome QTLs may partly account for the fact that domestication and breeding of tetraploid cottons has resulted in fiber yield and quality levels superior to those achieved by parallel improvement of “A” genome diploid cottons. The merger of two genomes with different evolutionary histories in a common nucleus appears to offer unique avenues for phenotypic response to selection. This may partly compensate for reduction in quantitative variation associated with polyploid formation and be one basis for the prominence of polyploids among extant angiosperms. These findings impel molecular dissection of the roles of divergent subgenomes in quantitative inheritance in many other polyploids and further exploration of both “synthetic” polyploids and exotic diploid genotypes for agriculturally useful variation.

Microevolutionary responses in experimental populations of plants to CO2-enriched environments: parallel results from two model systems.

Despite the critical role that terrestrial vegetation plays in the Earth's carbon cycle, very little is known about the potential evolutionary responses of plants to anthropogenically induced increases in concentrations of atmospheric CO2. We present experimental evidence that rising CO2 concentration may have a direct impact on the genetic composition and diversity of plant populations but is unlikely to result in selection favoring genotypes that exhibit increased productivity in a CO2-enriched atmosphere. Experimental populations of an annual plant (Abutilon theophrasti, velvetleaf) and a temperate forest tree (Betula alleghaniensis, yellow birch) displayed responses to increased CO2 that were both strongly density-dependent and genotype-specific. In competitive stands, a higher concentration of CO2 resulted in pronounced shifts in genetic composition, even though overall CO2-induced productivity enhancements were small. For the annual species, quantitative estimates of response to selection under competition were 3 times higher at the elevated CO2 level. However, genotypes that displayed the highest growth responses to CO2 when grown in the absence of competition did not have the highest fitness in competitive stands. We suggest that increased CO2 intensified interplant competition and that selection favored genotypes with a greater ability to compete for resources other than CO2. Thus, while increased CO2 may enhance rates of selection in populations of competing plants, it is unlikely to result in the evolution of increased CO2 responsiveness or to operate as an important feedback in the global carbon cycle. However, the increased intensity of selection and drift driven by rising CO2 levels may have an impact on the genetic diversity in plant populations.

Parallel evolution of sexual isolation in sticklebacks

Mechanisms of speciation are not well understood, despite decades of study. Recent work has focused on how natural and sexual selection cause sexual isolation. Here, we investigate the roles of divergent natural and sexual selection in the evolution of sexual isolation between sympatric species of threespine sticklebacks. We test the importance of morphological and behavioral traits in conferring sexual isolation and examine to what extent these traits have diverged in parallel between multiple, independently evolved species pairs. We use the patterns of evolution in ecological and mating traits to infer the likely nature of selection on sexual isolation. Strong parallel evolution implicates ecologically based divergent natural and/or sexual selection, whereas arbitrary directionality implicates nonecological sexual selection or drift. In multiple pairs we find that sexual isolation arises in the same way: assortative mating on body size and asymmetric isolation due to male nuptial color. Body size and color have diverged in a strongly parallel manner, similar to ecological traits. The data implicate ecologically based divergent natural and sexual selection as engines of speciation in this group.

Divergent selection and the evolution of signal traits and mating preferences

Mating preferences are common in natural populations, and their divergence among populations is considered an important source of reproductive isolation during speciation. Although mechanisms for the divergence of mating preferences have received substantial theoretical treatment, complementary experimental tests are lacking. We conducted a laboratory evolution experiment, using the fruit fly Drosophila serrata, to explore the role of divergent selection between environments in the evolution of female mating preferences. Replicate populations of D. serrata were derived from a common ancestor and propagated in one of three resource environments: two novel environments and the ancestral laboratory environment. Adaptation to both novel environments involved changes in cuticular hydrocarbons, traits that predict mating success in these populations. Furthermore, female mating preferences for these cuticular hydrocarbons also diverged among populations. A component of this divergence occurred among treatment environments, accounting for at least 17.4% of the among- population divergence in linear mating preferences and 17.2% of the among-population divergence in nonlinear mating preferences. The divergence of mating preferences in correlation with environment is consistent with the classic by- product model of speciation in which premating isolation evolves as a side effect of divergent selection adapting populations to their different environments.