Parallel Genetic Algorithms for calibrating Cellular Automata models: Application to lava flows

Cellular Automata are highly nonlinear dynamical systems which are suitable for simulating natural phenomena whose behaviour may be specified in terms of local interactions. The Cellular Automata model SCIARA, developed for the simulation of lava flows, demonstrated to be able to reproduce the behaviour of Etnean events. However, in order to apply the model for the prediction of future scenarios, a thorough calibrating phase is required. This work presents the application of Genetic Algorithms, general-purpose search algorithms inspired to natural selection and genetics, for the parameters optimisation of the modelSCIARA. Difficulties due to the elevated computational time suggested the adoption a Master-Slave Parallel Genetic Algorithm for the calibration of the model with respect to the 2001 Mt. Etna eruption. Results demonstrated the usefulness of the approach, both in terms of computing time and quality of performed simulations

The impact of equilibrium assumptions on tests of selection

With the increasing availability and quality of whole genome population data, various methodologies of population genetic inference are being utilized in order to identify and quantify recent population-level selective events. Though there has been a great proliferation of such methodology, the type-I and type-II error rates of many proposed statistics have not been well-described. Moreover, the performance of these statistics is often not evaluated for different biologically relevant scenarios (e.g., population size change, population structure), nor for the effect of differing data sizes (i.e., genomic vs. sub-genomic). The absence of the above information makes it difficult to evaluate newly available statistics relative to one another, and thus, difficult to choose the proper toolset for a given empirical analysis. Thus, we here describe and compare the performance of four widely used tests of selection: SweepFinder, SweeD, OmegaPlus, and iHS. In order to consider the above questions, we utilize simulated data spanning a variety of selection coefficients and beneficial mutation rates. We demonstrate that the LD-based OmegaPlus performs best in terms of power to reject the neutral model under both equilibrium and non-equilibrium conditions-an important result regarding the relative effectiveness of linkage disequilibrium relative to site frequency spectrum based statics. The results presented here ought to serve as a useful guide for future empirical studies, and provides a guide for statistical choice depending on the history of the population under consideration. Moreover, the parameter space investigated and the Type-I and Type-II error rates calculated, represent a natural benchmark by which future statistics may be assessed

Lichens as bioindicators of atmospheric heavy metal deposition in Valencia, Spain

The lichens due to their symbiotic nature have unique characteristics that confer them a key role as bioindicators of the environmental contamination. Many investigations have been done using epiphytic lichens as bioindicators, but only a few of these studies have used epilithiccrustose lichens. Three different epilithic-crustose lichens species: Candelariella sp., Lecanora sp. and Caloplaca sp. were studied as bioindicators of V, Cr,Mn, Co, Ni, Cu, Zn, As, Rb, Sr,Mo, Cd, Sb, Ba, Pb, Bi and U trace elements. Inductively Coupled Plasma Mass Spectrometry routine procedure is used to determining these element concentrations. Two sites were selected for lichens sampling according to environmental contamination. The lichens were collected on the facade of the Santos Juanes church in an urban area of Valencia; and on the rural area of Albarracin. The main aim of this work is showing the efficacy of the epilithic-crustose lichens as bioindicators of the air pollution. This study shows that the city of Valencia, compared with the rural area has high levels of Cu and Pb as detected using lichens as bioindicators. Therefore on the basis of these results, it can be hypothesized that Candelariella sp., Lecanora sp. and Caloplaca sp. are good accumulators of air borne heavy metals.This work has financial support of the Spanish Ministerio de Ciencia e Innovacion with a Ph.D. scholarship for Pilar Bosch Roig (BES-2006-12110) and with a three months stay scholarship to do this research in the Opificio delle Pietre Dure in Florence, Italy with Doctor Carlo Lalli. The authors wish to thank to the priest of the Santos Juanes Church of Valencia; the Direccion General de Patrimonio; to Prof. Pilar Roig Picazo and Prof. Ignacio Bosch Reig; the Instituto Universitario de Restauracion del Patrimonio from the Universitat Politecnica de Valencia; to the Ministero per I Beni Culturali; Italy.Bosch Roig, MDP.; Barca, D.; Crisci, G.; Lalli Galliano, C. (2013). 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Recent Progress in Polymorphism-Based Population Genetic Inference

The recent availability of whole-genome sequencing data affords tremendous power for statistical inference. With this, there has been great interest in the development of polymorphism-based approaches for the estimation of population genetic parameters. These approaches seek to estimate, for example, recently fixed or sweeping beneficial mutations, the rate of recurrent positive selection, the distribution of selection coefficients, and the demographic history of the population. Yet despite estimating similar parameters using similar data sets, results between methodologies are far from consistent. We here summarize the current state of the field, compare existing approaches, and attempt to reconcile emerging discrepancies. We also discuss the biases in selection estimators introduced by ignoring the demographic history of the population, discuss the biases in demographic estimators introduced by assuming neutrality, and highlight the important challenge to the field of achieving a true joint estimation procedure to circumvent these confounding effect

Quantifying polymorphism and divergence from epigenetic data: a framework for inferring the action of selection

Epigenetic modifications are alterations that regulate gene expression without modifying the underlying DNA sequence. DNA methylation and histone modifications, for example, are capable of spatial and temporal regulation of expression-with several studies demonstrating that these epigenetic marks are heritable. Thus, like DNA sequence, epigenetic marks are capable of storing information and passing it from one generation to the next. Because the epigenome is dynamic and epigenetic modifications can respond to external environmental stimuli, such changes may play an important role in adaptive evolution. While recent studies provide strong evidence for species-specific signatures of epigenetic marks, little is known about the mechanisms by which such modifications evolve. In order to address this question, we analyze the genome wide distribution of an epigenetic histone mark (H3K4me3) in prefrontal cortex neurons of humans, chimps and rhesus macaques. We develop a novel statistical framework to quantify within- and between-species variation in histone methylation patterns, using an ANOVA-based method and defining an FST -like measure for epigenetics (termed epi- FST), in order to develop a deeper understanding of the evolutionary pressures acting on epigenetic variation. Results demonstrate that genes with high epigenetic FST values are indeed significantly overrepresented among genes that are differentially expressed between species, and we observe only a weak correlation with SNP density

Regional manifestation of the widespread disruption of soil-landscapes by the 4kyr BP impact-linked dust event using pedo-sedimentary micro-fabrics

The co-occurrence of a sharp dust peak, low lake levels, forest reduction, and ice retreat at ca. 4-kyr BP throughout tropical Africa and West Asia have been widely explained as the effect of an abrupt climate change. The detailed study of soils and archaeological records provided evidence to re-interpret the 4 kyr BP dust event linked rather to the fallback of an impact-ejecta, but not climate change. Here we aim to further investigate the exceptional perturbation of the soil-landscapes widely initiated by the 4 kyr BP dust event. Results are based on soil data from the eastern Khabur basin (North-East Syria), the Vera Basin (Spain), and the lower Moche Valley (West Peru) compared with a new study at the reference site of Ebeon (West France). The quality of the 4 kyr BP dust signal and the related environmental records are investigated through a micromorphological study of pedo-sedimentary micro-fabrics combined with SEM-microprobe, mineralogical, and geochemical analyses.In the four regions studied, the intact 4 kyr BP signal is identifi ed as a discontinuous burnt soil surface with an exotic dust assemblage assigned to the distal fallout of an impact-ejecta. Its unusual two-fold micro-facies is interpreted as (1) flash heating due to pulverization of the hot ejecta cloud at the soil surface, and (2) high energy deflation caused by the impact-related air blast. Disruption of the soil surface is shown to have been rapidly followed by a major de-stabilisation of the soil cover. Local factors and regional settings have exerted a major control on the timing, duration, and magnitude of landscape disturbances. Studies showed how a high quality signal allows to discriminate the short-term severe landscape disturbances linked to the exceptional 4 kyr BP dust event from more gradual environmental changes triggered by climate shift at the same time

Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease

Potential Celiac Patients (PCD) bear the Celiac Disease (CD) genetic predisposition, a significant production of antihuman transglutaminase antibodies, but no morphological changes in the small bowel mucosa. A minority of patients (17%) showed clinical symptoms and need a gluten free diet at time of diagnosis, while the majority progress over several years (up to a decade) without any clinical problem neither a progression of the small intestine mucosal damage even when they continued to assume gluten in their diet. Recently we developed a traditional multivariate approach to predict the natural history, on the base of the information at enrolment (time 0) by a discriminant analysis model. Still, the traditional multivariate model requires stringent assumptions that may not be answered in the clinical setting. Starting from a follow-up dataset available for PCD, we propose the application of Machine Learning (ML) methodologies to extend the analysis on available clinical data and to detect most influent features predicting the outcome. These features, collected at time of diagnosis, should be capable to classify patients who will develop duodenal atrophy from those who will remain potential. Four ML methods were adopted to select features predictive of the outcome; the feature selection procedure was indeed capable to reduce the number of overall features from 85 to 19. ML methodologies (Random Forests, Extremely Randomized Trees, and Boosted Trees, Logistic Regression) were adopted, obtaining high values of accuracy: all report an accuracy above 75%. The specificity score was always more than 75% also, with two of the considered methods over 98%, while the best performance of sensitivity was 60%. The best model, optimized Boosted Trees, was able to classify PCD starting from the selected 19 features with an accuracy of 0.80, sensitivity of 0.58 and specificity of 0.84. Finally, with this work, we are able to categorize PCD patients that can more likely develop overt CD using ML. ML techniques appear to be an innovative approach to predict the outcome of PCD, since they provide a step forward in the direction of precision medicine aimed to customize healthcare, medical therapies, decisions, and practices tailoring the clinical management of PCD children

Targeted metabolomic profiling in rat tissues reveals sex differences

Sex differences affect several diseases and are organ-and parameter-specific. In humans and animals, sex differences also influence the metabolism and homeostasis of amino acids and fatty acids, which are linked to the onset of diseases. Thus, the use of targeted metabolite profiles in tissues represents a powerful approach to examine the intermediary metabolism and evidence for any sex differences. To clarify the sex-specific activities of liver, heart and kidney tissues, we used targeted metabolomics, linear discriminant analysis (LDA), principal component analysis (PCA), cluster analysis and linear correlation models to evaluate sex and organ-specific differences in amino acids, free carnitine and acylcarnitine levels in male and female Sprague-Dawley rats. Several intra-sex differences affect tissues, indicating that metabolite profiles in rat hearts, livers and kidneys are organ-dependent. Amino acids and carnitine levels in rat hearts, livers and kidneys are affected by sex: male and female hearts show the greatest sexual dimorphism, both qualitatively and quantitatively. Finally, multivariate analysis confirmed the influence of sex on the metabolomics profiling. Our data demonstrate that the metabolomics approach together with a multivariate approach can capture the dynamics of physiological and pathological states, which are essential for explaining the basis of the sex differences observed in physiological and pathological conditions

Work function tuning in hydrothermally synthesized vanadium-doped MoO3 and Co3O4 mesostructures for energy conversion devices

The wide interest in developing green energy technologies stimulates the scientific community to seek, for devices, new substitute material platforms with a low environmental impact, ease of production and processing and long-term stability. The synthesis of metal oxide (MO) semiconductors fulfils these requirements and efforts are addressed towards optimizing their functional properties through the improvement of charge mobility or energy level alignment. Two MOs have rising perspectives for application in light harvesting devices, mainly for the role of charge selective layers but also as light absorbers, namely MoO3 (an electron blocking layer) and Co3O4 (a small band gap semiconductor). The need to achieve better charge transport has prompted us to explore strategies for the doping of MoO3 and Co3O4 with vanadium (V) ions that, when combined with oxygen in V2O5, produce a high work function MO. We report on subcritical hydrothermal synthesis of V-doped mesostructures of MoO3 and of Co3O4, in which a tight control of the doping is exerted by tuning the relative amounts of reactants. We accomplished a full analytical characterization of these V-doped MOs that unambiguously demonstrates the incorporation of the vanadium ions in the host material, as well as the effects on the optical properties and work function. We foresee a promising future use of these materials as charge selective materials in energy devices based on multilayer structures