Search results for " selection"

Life History Trade-Offs and Relaxed Selection Can Decrease Bacterial Virulence in Environmental Reservoirs

2012

Pathogen virulence is usually thought to evolve in reciprocal selection with the host. While this might be true for obligate pathogens, the life histories of opportunistic pathogens typically alternate between within-host and outside-host environments during the infection-transmission cycle. As a result, opportunistic pathogens are likely to experience conflicting selection pressures across different environments, and this could affect their virulence through life-history trait correlations. We studied these correlations experimentally by exposing an opportunistic bacterial pathogen Serratia marcescens to its natural protist predator Tetrahymena thermophila for 13 weeks, after which we meas…

Advantage of rare infanticide strategies in an invasion experiment of behavioural polymorphism

2012

Killing conspecific infants (infanticide) is among the most puzzling phenomena in nature. Stable polymorphism in such behaviour could be maintained by negative frequency-dependent selection (benefit of rare types). However, it is currently unknown whether there is genetic polymorphism in infanticidal behaviour or whether infanticide may have any fitness advantages when rare. Here we show genetic polymorphism in non-parental infanticide. Our novel invasion experiment confirms negative frequency-dependent selection in wild bank vole populations, where resource benefits allow an infanticidal strategy to invade a population of non-infanticidal individuals. The results show that infanticidal beh…

Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning

2014

Mango fruit are sensitive and can easily develop brown spots after suffering mechanical stress during postharvest handling, transport and marketing. The manual inspection of this fruit used today cannot detect the damage in very early stages of maturity and to date no automatic tool capable of such detection has been developed, since current systems based on machine vision only detect very visible damage. The application of hyperspectral imaging to the postharvest quality inspection of fruit is relatively recent and research is still underway to find a method of estimating internal properties or detecting invisible damage. This work describes a new system to evaluate mechanically induced da…

Distributed Systems for Fusion of Visual Information

1997

Do Women Prefer More Complex Music around Ovulation?

2012

The evolutionary origins of music are much debated. One theory holds that the ability to produce complex musical sounds might reflect qualities that are relevant in mate choice contexts and hence, that music is functionally analogous to the sexually-selected acoustic displays of some animals. If so, women may be expected to show heightened preferences for more complex music when they are most fertile. Here, we used computer-generated musical pieces and ovulation predictor kits to test this hypothesis. Our results indicate that women prefer more complex music in general; however, we found no evidence that their preference for more complex music increased around ovulation. Consequently, our f…

Selección de personal basada en métodos difusos

2011

[ES] Las decisiones de los directivos en cuanto a la selección de personal determinan en gran medida el éxito de la empresa. Una elección adecuada de los empleados proporciona una ventaja comparativa. Proponemos un método borroso para la selección de personal basado en la gestión de competencias y la comparación con la valoración que la empresa considera más adecuada para cada trabajo (el candidato ideal). Nuestro método utiliza la distancia de Hamming y el Matching Level Index. Los algoritmos, implementados con el software Sta¿Designer, nos permite establecer un ranking de candidatos, incluso cuando las competencias del candidato ideal han sido evaluadas tan solo en parte. Nuestro enfoque …

Tuning parameter selection in LASSO regression

2016

We propose a new method to select the tuning parameter in lasso regression. Unlike the previous proposals, the method is iterative and thus it is particularly efficient when multiple tuning parameters have to be selected. The method also applies to more general regression frameworks, such as generalized linear models with non-normal responses. Simulation studies show our proposal performs well, and most of times, better when compared with the traditional Bayesian Information Criterion and Cross validation.

Spatiotemporal and gender-specific parasitism in two species of gobiid fish

2018

Parasitism is considered a major selective force in natural host populations. Infections can decrease host condition and vigour, and potentially influence, for example, host population dynamics and behavior such as mate choice. We studied parasite infections of two common marine fish species, the sand goby (Pomatoschistus minutus) and the common goby (Pomatoschistus microps), in the brackish water Northern Baltic Sea. We were particularly interested in the occurrence of parasite taxa located in central sensory organs, such as eyes, potentially affecting fish behavior and mate choice. We found that both fish species harbored parasite communities dominated by taxa transmitted to fish through …

Model selection for penalized Gaussian Graphical Models

2013

High-dimensional data refers to the case in which the number of parameters is of one or more order greater than the sample size. Penalized Gaussian graphical models can be used to estimate the conditional independence graph in high-dimensional setting. In this setting, the crucial issue is to select the tuning parameter which regulates the sparsity of the graph. In this paper, we focus on estimating the "best" tuning parameter. We propose to select this tuning parameter by minimizing an information criterion based on the generalized information criterion and to use a stability selection approach in order to obtain a more stable graph. The performance of our method is compared with the state…

A computational method to estimate sparse multiple Gaussian graphical models

2012

In recent years several researchers have proposed the use of the Gaussian graphical model defined on a high dimensional setting to explore the dependence relationships between random variables. Standard methods, usually proposed in literature, are based on the use of a specific penalty function, such as the L1-penalty function. In this paper our aim is to estimate and compare two or more Gaussian graphical models defined in a high dimensional setting. In order to accomplish our aim, we propose a new computational method, based on glasso method, which lets us to extend the notion of p-value.