The genomic basis of Red Queen dynamics during rapid reciprocal host–pathogen coevolution

Pathogens are omnipresent and by definition detrimental to their hosts. Pathogens thus exert high selection on their hosts, which, if adapting, can exert similar levels of selection on the pathogen, resulting in ongoing cycles of reciprocal adaptation between the antagonists. Such coevolutionary interactions have a central influence on the evolution of organisms. Surprisingly, we still know little about the exact selection dynamics and the genome regions involved. Our study uses a controlled experimental approach with an animal host to dissect coevolutionary selection. We find that distinct selective processes underlie rapid coadaptation in the two antagonists, including antagonistic frequency-dependent selection on toxin gene copy number in the pathogen, while the host response is likely influenced by changes in multiple genome regions.Red Queen dynamics, involving coevolutionary interactions between species, are ubiquitous, shaping the evolution of diverse biological systems. To date, information on the underlying selection dynamics and the involved genome regions is mainly available for bacteria–}phage systems or only one of the antagonists of a eukaryotic host{–}pathogen interaction. We add to our understanding of these important coevolutionary interactions using an experimental host{–pathogen model, which includes the nematode Caenorhabditis elegans and its pathogen Bacillus thuringiensis. We combined experimental evolution with time-shift experiments, in which a focal host or pathogen is tested against a coevolved antagonist from the past, present, or future, followed by genomic analysis. We show that (i) coevolution occurs rapidly within few generations, (ii) temporal coadaptation at the phenotypic level is found in parallel across replicate populations, consistent with antagonistic frequency-dependent selection, (iii) genomic changes in the pathogen match the phenotypic pattern and include copy number variations of a toxin-encoding plasmid, and (iv) host genomic changes do not match the phenotypic pattern and likely involve selective responses at more than one locus. By exploring the dynamics of coevolution at the phenotypic and genomic level for both host and pathogen simultaneously, our findings demonstrate a more complex model of the Red Queen, consisting of distinct selective processes acting on the two antagonists during rapid and reciprocal coadaptation

The genomic basis of Red Queen dynamics during rapid reciprocal host-pathogen coevolution

Red Queen dynamics, involving coevolutionary interactions between species, are ubiquitous, shaping the evolution of diverse biological systems. To date, information on the underlying selection dynamics and the involved genome regions is mainly available for bacteria-phage systems or only one of the antagonists of a eukaryotic host-pathogen interaction. We add to our understanding of these important coevolutionary interactions using an experimental host-pathogen model, which includes the nematode Caenorhabditis elegans and its pathogen Bacillus thuringiensis We combined experimental evolution with time-shift experiments, in which a focal host or pathogen is tested against a coevolved antagonist from the past, present, or future, followed by genomic analysis. We show that (i) coevolution occurs rapidly within few generations, (ii) temporal coadaptation at the phenotypic level is found in parallel across replicate populations, consistent with antagonistic frequency-dependent selection, (iii) genomic changes in the pathogen match the phenotypic pattern and include copy number variations of a toxin-encoding plasmid, and (iv) host genomic changes do not match the phenotypic pattern and likely involve selective responses at more than one locus. By exploring the dynamics of coevolution at the phenotypic and genomic level for both host and pathogen simultaneously, our findings demonstrate a more complex model of the Red Queen, consisting of distinct selective processes acting on the two antagonists during rapid and reciprocal coadaptation

Italian Text Categorization with Lemmatization and Support Vector Machines

The paper describes an Italian language text categorizer by Lemmatization and support vector machines. The categorizer is composed of six modules. The first module performs the tokenization, removing the punctuation signs; the second and third ones carry out stopping and lemmatization, respectively; the fourth module implements the bag-of-words approach; the fifth one performs feature dimensionality reduction eliminating poor discriminant features; finally, the last module does the classification. The Italian text categorizer has been validated on a database composed of more than 1100 articles, extracted from online edition of three Italian language newspapers, belonging to eight different categories. The work is highly novel, since to the best our knowledge, there are no works in literature on Italian text categorization

Filtrování nevyžádané pošty pomocí regularizovaných neuronových sítí s rektifikovanými lineárními jednotkami

The rapid growth of unsolicited and unwanted messages has inspired the development of many anti-spam methods. Machine-learning methods such as Naïve Bayes (NB), support vector machines (SVMs) or neural networks (NNs) have been particularly effective in categorizing spam /non-spam messages. They automatically construct word lists and their weights usually in a bag-of-words fashion. However, traditional multilayer perceptron (MLP) NNs usually suffer from slow optimization convergence to a poor local minimum and overfitting issues. To overcome this problem, we use a regularized NN with rectified linear units (RANN-ReL) for spam filtering. We compare its performance on three benchmark spam datasets (Enron, SpamAssassin, and SMS spam collection) with four machine algorithms commonly used in text classification, namely NB, SVM, MLP, and k-NN. We show that the RANN-ReL outperforms other methods in terms of classification accuracy, false negative and false positive rates. Notably, it classifies well both major (legitimate) and minor (spam) classes.Rychlý růst nevyžádaných a nežádoucích zpráv inspiroval vývoj mnoha anti-spamových metod. Metody strojového učení, jako je Naive Bayes (NB), podpůrné vektorové stroje (SVM) nebo neuronové sítě (NN) byly při kategorizaci spamu obzvláště účinné. Tyto metody automaticky sestavují seznamy slov a jejich váhy obvykle v módu balíků slov. Nicméně, tradiční vícevrstvý perceptron (MLP) obvykle trpí pomalou konvergencí ke horšímu lokálním minimu a problémem přeučení. K překonání tohoto problému používáme pro filtrování nevyžádané pošty regularizované NN s rektifikovanými lineárními jednotkami (RANN-ReL). Porovnáváme jejich výkon na třech testovacích datových sadách (Enron, SpamAssassin a SMS spamu) se čtyřmi algoritmy strojového učení běžně používaných v textovém klasifikaci, a to NB, SVM, MLP a k-NN. Ukázali jsme, že RANN-ReL překonává jiné metody pokud jde o přesnost klasifikace, chybně negativní a chybně pozitivní míry. Tento systém klasifikuje jak majoritní (oprávněné) tak minoritní (spam) třídy

Predictive power of Brazilian equity fund performance using R2 as a measure of selectivity

ABSTRACT This paper aimed to investigate the impact of levels of selectivity on the performance of equity funds using a methodology applied for the first time ever (as far as we know) in the Brazilian market. As an indicator of the activity level of a fund, we proposed the coefficient of determination (R2) of the regression of its returns over market returns. In total, 867 funds were analyzed in the period between November 2004 and October 2014. The hypothesis tested is that more selective funds perform better to compensate for their higher operating costs. This hypothesis was confirmed in the Brazilian market. Dynamic equally-weighted portfolios of funds were simulated, according to their past R2 and alphas, with monthly rebalancing and 12-month moving windows. The portfolio of the most selective funds had a Sharpe ratio of 0.0494, on a monthly basis, while the portfolio of the least selective funds had a Sharpe ratio of -0.0314. Performance was also higher in evaluations involving excess returns, Jensen’s alpha, and accumulated returns, as well as when compared to randomly selected portfolios. Moreover, past performance (as measured by Jensen’s alpha) was also a predictor of future performance. Particularly, the portfolio composed by funds with a higher past alpha and lower past R2 presented a Sharpe ratio of 0.1483 and a Jensen’s alpha of 0.87% (significant at 1%), while the one composed of funds with a lower past alpha and lower activity level presented a Sharpe ratio of -0.0673 and an alpha of -0.32% (also significant at 1%)