Are you my mother? Bayesian phylogenetic inference of recombination among putative parental strains

Reconstructing evolutionary relationships using Bayesian inference has become increasingly popular due to the ability of Bayesian inference to handle complex models of evolution. In this review we concentrate on inference of recombination events between strains of viruses when these events are sporadic, ie rare relative to point mutations. Bayesian inference is especially attractive in the detection of recombination events because it allows for simultaneous inferences about the presence, number and location of crossover points and the identification of parental sequences. Current frequentist recombination identification falls into a sequential testing trap. The most likely parental sequences and crossover points are identified using the data and then the certainty of recombination is assessed conditional on this identification. After briefly outlining basic phylogenetic models, Bayesian inference and Markov chain Monte Carlo (MCMC) computation, we summarise three different approaches to recombination detection and discuss current challenges in applying Bayesian phylogenetic inference of recombination.     

Real-time odor discrimination using a bioelectronic sensor array based on the insect electroantennogram

Current trends in artificial nose research are strongly influenced by knowledge of biological olfactory systems. Insects have evolved over millions of years to detect and maneuver toward a food source or mate, or away from predators. The insect olfactory system is able to identify volatiles on a time scale that matches their ability to maneuver. Here, biological olfactory sense organs, insect antennae, have been exploited in a hybrid-device biosensor, demonstrating the ability to identify individual strands of odor in a plume passing over the sensor on a sub-second time scale. A portable system was designed to utilize the electrophysiological responses recorded from a sensor array composed of male or female antennae from four or eight different species of insects (a multi-channel electroantennogram, EAG). A computational analysis strategy that allows discrimination between odors in real time is described in detail. Following a training period, both semi-parametric and k-nearest neighbor (k-NN) classifiers with the ability to discard ambiguous responses are applied toward the classification of up to eight odors. EAG responses to individual strands in an odor plume are classified or discarded as ambiguous with a delay (sensor response to classification report) on the order of 1 s. The dependence of classification error rate on several parameters is described. Finally, the performance of the approach is compared to that of a minimal conditional risk classifier.     

A Primer on Bayesian Inference for Biophysical Systems

Bayesian inference is a powerful statistical paradigm that has gained popularity in many fields of science, but adoption has been somewhat slower in biophysics. Here, I provide an accessible tutorial on the use of Bayesian methods by focusing on example applications that will be familiar to biophysicists. I first discuss the goals of Bayesian inference and show simple examples of posterior inference using conjugate priors. I then describe Markov chain Monte Carlo sampling and, in particular, discuss Gibbs sampling and Metropolis random walk algorithms with reference to detailed examples. These Bayesian methods (with the aid of Markov chain Monte Carlo sampling) provide a generalizable way of rigorously addressing parameter inference and identifiability for arbitrarily complicated models.     

Electroantennogram (EAG) responses of Microplitis croceipes and Cotesia marginiventris and their lepidopteran hosts to a wide array of odor stimuli: Correlation between EAG response and degree of host specificity?

In order to test whether the electroantennogram (EAG) response spectrum of an insect correlates to its degree of host specificity, we recorded EAG responses of two parasitoid species with different degrees of host specificity, Microplitis croceipes (specialist) and Cotesia marginiventris (generalist), to a wide array of odor stimuli including compounds representing green leaf volatiles (GLVs), herbivore-induced plant volatiles (HIPV), ecologically irrelevant (not used by the parasitoid species and their hosts for host location) plant volatiles, and host-specific odor stimuli (host sex pheromones, and extracts of host caterpillar body and frass). We also tested the EAG responses of female moths of the caterpillar hosts of the parasitoids, Heliothis virescens and Spodoptera exigua, to some of the odor stimuli. We hypothesized that the specialist parasitoid will have a narrower EAG response spectrum than the generalist, and that the two lepidopteran species, which are similar in their host plant use, will show similar EAG response spectra to plant volatiles. As predicted, the specialist parasitoid showed greater EAG responses than the generalist to host-specific odor and one HIPV (cis-3-hexenyl butyrate), whereas the generalist showed relatively greater EAG responses to the GLVs and unrelated plant volatiles. We detected no differences in the EAG responses of H. virescens and S. exigua to any of the tested odor.     

Behavioral and Electroantennogram Responses of Phorid fly <Emphasis Type="Italic">Pseudacteon tricuspis</Emphasis> (Diptera: Phoridae) to Red Imported Fire Ant <Emphasis Type="Italic">Solenopsis invicta</Emphasis> Odor and Trail Pheromone

The phorid fly, Pseudacteon tricuspis is an introduced parasitoid of the red imported fire ant, Solenopsis invicta in the United States. Previous studies show that phorid flies are attracted to host ant workers at disturbed colonies, to colonies exhibiting aggressive interspecific interactions, and to fire ant mating flights. In a series of behavioral and electroantennogram (EAG) experiments, we confirm the possible use of fire ant odor as cues for host location by P. tricuspis. We tested the response of P. tricuspis of different sex and mating status to several host-related odor stimuli including live fire ant workers, extracts of worker whole body, head, thorax, and abdomen, and (E,E)-α-farnesene, a trail pheromone component of Solenopsis fire ants. Results from Y-tube olfactometer bioassays demonstrated the attraction of mated female P. tricuspis to live S. invicta workers. In addition, extracts of S. invicta worker whole body and thorax elicited strong olfactometer response in female flies (mated and unmated) and mated males, but not in unmated males. Pseudacteon tricuspis did not show significant attraction to extracts of S. invicta worker head and abdomen, or to (E,E)-α-farnesene, irrespective of sex and mating status. In EAG experiments, female and male P. tricuspis showed significant EAG response to extracts of worker whole body, head, and abdomen, and to a less extent, thorax extract, but not to (E,E)-α-farnesene. Females showed slightly greater EAG response than males, but EAG response was not affected by mating status. These results suggest that fire ant thorax is likely the source of kairomones used as host location cues by P. tricuspis, and support the hypothesis that fire ant worker trail pheromones are not likely used by P. tricuspis for host location.     

Defining Interaction between Electroantennogram Responses of Epiphyas postvittana (lepidoptera: Tortricidae) to Pheromone and Other Volatiles

The potential for electroantennograms (EAGs) to assist in the measurement of atmospheric pheromone concentrations was examined in laboratory and field experiments by using multiple stimuli, the main component of the pheromone of Epiphyas postvittana, (E)-11-tetradecenyl acetate, its Z-isomer (a behavioral antagonist), and alpha-terpineol (a representative host-plant odor) were presented to the antenna simultaneously to simulate field conditions. The EAG results were compared with predictions from two models describing responses to combined stimuli. Responses are defined as log-additive if they can be described with the equation [EAG((total)) =a (log ([P(x)]+[B(y)])+c] and as linear-additive if the EAG follows the equation [EAG((total)) =a (log [P(x)])+c+a'(log [B(y)])+c'] where [B(y)] is the concentration of the stimulant background odor and [P(x)] is the concentration of an additional odor stimulus. The EAGs elicited by the added stimuli were inversely related to the concentration of the volatile in the background. EAGs elicited by all combined stimuli followed the log-additive model. Our laboratory results were validated in field tests; alpha-terpineol represents the volatiles present in orchard air. In spite of this interaction between the perception of pheromone and plant volatiles, the field EAG can be used for the measurement of atmospheric pheromone concentrations, where background odor concentrations are relatively constant.     

Probabilistic methods surpass parsimony when assessing clade support in phylogenetic analyses of discrete morphological data

Fossil taxa are critical to inferences of historical diversity and the origins of modern biodiversity, but realizing their evolutionary significance is contingent on restoring fossil species to their correct position within the tree of life. For most fossil species, morphology is the only source of data for phylogenetic inference; this has traditionally been analysed using parsimony, the predominance of which is currently challenged by the development of probabilistic models that achieve greater phylogenetic accuracy. Here, based on simulated and empirical datasets, we explore the relative efficacy of competing phylogenetic methods in terms of clade support. We characterize clade support using bootstrapping for parsimony and Maximum Likelihood, and intrinsic Bayesian posterior probabilities, collapsing branches that exhibit less than 50% support. Ignoring node support, Bayesian inference is the most accurate method in estimating the tree used to simulate the data. After assessing clade support, Bayesian and Maximum Likelihood exhibit comparable levels of accuracy, and parsimony remains the least accurate method. However, Maximum Likelihood is less precise than Bayesian phylogeny estimation, and Bayesian inference recaptures more correct nodes with higher support compared to all other methods, including Maximum Likelihood. We assess the effects of these findings on empirical phylogenies. Our results indicate probabilistic methods should be favoured over parsimony.     

Multisensory Oddity Detection as Bayesian Inference

A key goal for the perceptual system is to optimally combine information from all the senses that may be available in order to develop the most accurate and unified picture possible of the outside world. The contemporary theoretical framework of ideal observer maximum likelihood integration (MLI) has been highly successful in modelling how the human brain combines information from a variety of different sensory modalities. However, in various recent experiments involving multisensory stimuli of uncertain correspondence, MLI breaks down as a successful model of sensory combination. Within the paradigm of direct stimulus estimation, perceptual models which use Bayesian inference to resolve correspondence have recently been shown to generalize successfully to these cases where MLI fails. This approach has been known variously as model inference, causal inference or structure inference. In this paper, we examine causal uncertainty in another important class of multi-sensory perception paradigm – that of oddity detection and demonstrate how a Bayesian ideal observer also treats oddity detection as a structure inference problem. We validate this approach by showing that it provides an intuitive and quantitative explanation of an important pair of multi-sensory oddity detection experiments – involving cues across and within modalities – for which MLI previously failed dramatically, allowing a novel unifying treatment of within and cross modal multisensory perception. Our successful application of structure inference models to the new ‘oddity detection’ paradigm, and the resultant unified explanation of across and within modality cases provide further evidence to suggest that structure inference may be a commonly evolved principle for combining perceptual information in the brain.     

Dependence among sites in RNA evolution

Although probabilistic models of genotype (e.g., DNA sequence) evolution have been greatly elaborated, less attention has been paid to the effect of phenotype on the evolution of the genotype. Here we propose an evolutionary model and a Bayesian inference procedure that are aimed at filling this gap. In the model, RNA secondary structure links genotype and phenotype by treating the approximate free energy of a sequence folded into a secondary structure as a surrogate for fitness. The underlying idea is that a nucleotide substitution resulting in a more stable secondary structure should have a higher rate than a substitution that yields a less stable secondary structure. This free energy approach incorporates evolutionary dependencies among sequence positions beyond those that are reflected simply by jointly modeling change at paired positions in an RNA helix. Although there is not a formal requirement with this approach that secondary structure be known and nearly invariant over evolutionary time, computational considerations make these assumptions attractive and they have been adopted in a software program that permits statistical analysis of multiple homologous sequences that are related via a known phylogenetic tree topology. Analyses of 5S ribosomal RNA sequences are presented to illustrate and quantify the strong impact that RNA secondary structure has on substitution rates. Analyses on simulated sequences show that the new inference procedure has reasonable statistical properties. Potential applications of this procedure, including improved ancestral sequence inference and location of functionally interesting sites, are discussed.     

On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread

Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.     

Accuracy of rate estimation using relaxed-clock models with a critical focus on the early metazoan radiation

In recent years, a number of phylogenetic methods have been developed for estimating molecular rates and divergence dates under models that relax the molecular clock constraint by allowing rate change throughout the tree. These methods are being used with increasing frequency, but there have been few studies into their accuracy. We tested the accuracy of several relaxed-clock methods (penalized likelihood and Bayesian inference using various models of rate change) using nucleotide sequences simulated on a nine-taxon tree. When the sequences evolved with a constant rate, the methods were able to infer rates accurately, but estimates were more precise when a molecular clock was assumed. When the sequences evolved under a model of auto-correlated rate change, rates were accurately estimated using penalized likelihood and by Bayesian inference using lognormal and exponential models of rate change, while other models did not perform as well. When the sequences evolved under a model of uncorrelated rate change, only Bayesian inference using an exponential rate model performed well. Collectively, the results provide a strong recommendation for using the exponential model of rate change if a conservative approach to divergence time estimation is required. A case study is presented in which we use a simulation-based approach to examine the hypothesis of elevated rates in the Cambrian period, and it is found that these high rate estimates might be an artifact of the rate estimation method. If this bias is present, then the ages of metazoan divergences would be systematically underestimated. The results of this study have implications for studies of molecular rates and divergence dates.     

Can we continue to neglect genomic variation in introgression rates when inferring the history of speciation? A case study in a Mytilus hybrid zone

The use of molecular data to reconstruct the history of divergence and gene flow between populations of closely related taxa represents a challenging problem. It has been proposed that the long‐standing debate about the geography of speciation can be resolved by comparing the likelihoods of a model of isolation with migration and a model of secondary contact. However, data are commonly only fit to a model of isolation with migration and rarely tested against the secondary contact alternative. Furthermore, most demographic inference methods have neglected variation in introgression rates and assume that the gene flow parameter (Nm) is similar among loci. Here, we show that neglecting this source of variation can give misleading results. We analysed DNA sequences sampled from populations of the marine mussels, Mytilus edulis and M. galloprovincialis, across a well‐studied mosaic hybrid zone in Europe and evaluated various scenarios of speciation, with or without variation in introgression rates, using an Approximate Bayesian Computation (ABC) approach. Models with heterogeneous gene flow across loci always outperformed models assuming equal migration rates irrespective of the history of gene flow being considered. By incorporating this heterogeneity, the best‐supported scenario was a long period of allopatric isolation during the first three‐quarters of the time since divergence followed by secondary contact and introgression during the last quarter. By contrast, constraining migration to be homogeneous failed to discriminate among any of the different models of gene flow tested. Our simulations thus provide statistical support for the secondary contact scenario in the European Mytilus hybrid zone that the standard coalescent approach failed to confirm. Our results demonstrate that genomic variation in introgression rates can have profound impacts on the biological conclusions drawn from inference methods and needs to be incorporated in future studies.     

A Part-Based Probabilistic Model for Object Detection with Occlusion

The part-based method has been a fast rising framework for object detection. It is attracting more and more attention for its detection precision and partial robustness to the occlusion. However, little research has been focused on the problem of occlusion overlapping of the part regions, which can reduce the performance of the system. This paper proposes a part-based probabilistic model and the corresponding inference algorithm for the problem of the part occlusion. The model is based on the Bayesian theory integrally and aims to be robust to the large occlusion. In the stage of the model construction, all of the parts constitute the vertex set of a fully connected graph, and a binary variable is assigned to each part to indicate its occlusion status. In addition, we introduce a penalty term to regularize the argument space of the objective function. Thus, the part detection is formulated as an optimization problem, which is divided into two alternative procedures: the outer inference and the inner inference. A stochastic tentative method is employed in the outer inference to determine the occlusion status for each part. In the inner inference, the gradient descent algorithm is employed to find the optimal positions of the parts, in term of the current occlusion status. Experiments were carried out on the Caltech database. The results demonstrated that the proposed method achieves a strong robustness to the occlusion.     

Joint reconstruction of divergence times and life-history evolution in placental mammals using a phylogenetic covariance model

Violation of the molecular clock has been amply documented, and is now routinely taken into account by molecular dating methods. Comparative analyses have revealed a systematic component in rate variation, relating it to the evolution of life-history traits, such as body size or generation time. Life-history evolution can be reconstructed using Brownian models. However, the resulting estimates are typically uncertain, and potentially sensitive to the underlying assumptions. As a way of obtaining more accurate ancestral trait and divergence time reconstructions, correlations between life-history traits and substitution rates could be used as an additional source of information. In this direction, a Bayesian framework for jointly reconstructing rates, traits, and dates was previously introduced. Here, we apply this model to a 17 protein-coding gene alignment for 73 placental taxa. Our analysis indicates that the coupling between molecules and life history can lead to a reevaluation of ancestral life-history profiles, in particular for groups displaying convergent evolution in body size. However, reconstructions are sensitive to fossil calibrations and to the Brownian assumption. Altogether, our analysis suggests that further integrating inference of rates and traits might be particularly useful for neontological macroevolutionary comparative studies.     

Precision gain versus effort with joint models using detection/non‐detection and banding data

Capture–recapture techniques provide valuable information, but are often more cost‐prohibitive at large spatial and temporal scales than less‐intensive sampling techniques. Model development combining multiple data sources to leverage data source strengths and for improved parameter precision has increased, but with limited discussion on precision gain versus effort. We present a general framework for evaluating trade‐offs between precision gained and costs associated with acquiring multiple data sources, useful for designing future or new phases of current studies.We illustrated how Bayesian hierarchical joint models using detection/non‐detection and banding data can improve abundance, survival, and recruitment inference, and quantified data source costs in a northern Arizona, USA, western bluebird (Sialia mexicana) population. We used an 8‐year detection/non‐detection (distributed across the landscape) and banding (subset of locations within landscape) data set to estimate parameters. We constructed separate models using detection/non‐detection and banding data, and a joint model using both data types to evaluate parameter precision gain relative to effort.Joint model parameter estimates were more precise than single data model estimates, but parameter precision varied (apparent survival > abundance > recruitment). Banding provided greater apparent survival precision than detection/non‐detection data. Therefore, little precision was gained when detection/non‐detection data were added to banding data. Additional costs were minimal; however, additional spatial coverage and ability to estimate abundance and recruitment improved inference. Conversely, more precision was gained when adding banding to detection/non‐detection data at higher cost. Spatial coverage was identical, yet survival and abundance estimates were more precise. Justification of increased costs associated with additional data types depends on project objectives.We illustrate a general framework for evaluating precision gain relative to effort, applicable to joint data models with any data type combination. This framework evaluates costs and benefits from and effort levels between multiple data types, thus improving population monitoring designs.     

Neurocomputational mechanism of controllability inference under a multi-agent setting

Controllability perception significantly influences motivated behavior and emotion and requires an estimation of one’s influence on an environment. Previous studies have shown that an agent can infer controllability by observing contingency between one’s own action and outcome if there are no other outcome-relevant agents in an environment. However, if there are multiple agents who can influence the outcome, estimation of one’s genuine controllability requires exclusion of other agents’ possible influence. Here, we first investigated a computational and neural mechanism of controllability inference in a multi-agent setting. Our novel multi-agent Bayesian controllability inference model showed that other people’s action-outcome contingency information is integrated with one’s own action-outcome contingency to infer controllability, which can be explained as a Bayesian inference. Model-based functional MRI analyses showed that multi-agent Bayesian controllability inference recruits the temporoparietal junction (TPJ) and striatum. Then, this inferred controllability information was leveraged to increase motivated behavior in the vmPFC. These results generalize the previously known role of the striatum and vmPFC in single-agent controllability to multi-agent controllability, and this generalized role requires the TPJ in addition to the striatum of single-agent controllability to integrate both self- and other-related information. Finally, we identified an innate positive bias toward the self during the multi-agent controllability inference, which facilitated behavioral adaptation under volatile controllability. Furthermore, low positive bias and high negative bias were associated with increased daily feelings of guilt. Our results provide a mechanism of how our sense of controllability fluctuates due to other people in our lives, which might be related to social learned helplessness and depression.     

Habitat cues synergize to elicit chemically mediated landing behavior in a specialist phytophagous insect,the grape berry moth

Many phytophagous insects locate their host plant using mixtures of volatile compounds produced by the plant. A key behavior in the host location process that has been the focus of decades of behavioral research is optomotor anemotaxis. Another key step in host location is landing on (or near) the odor source. In previous work, rubber septa emitting a synthetic blend of volatiles extracted from young shoots of grape plants, Vitus spp. (Vitaceae), elicited equivalent levels of oriented upwind flight by female grape berry moths (GBM), Paralobesia viteana (Clemens) (Lepidoptera: Tortricidae), as did actual (control) grape shoots. However, in contrast to the shoots, females did not land on the odor source. In this study, we used flight tunnel assays to investigate the landing response of GBM females with respect to chemical and visual stimuli, as well as differences in relative humidity. When stimuli were presented individually, only the synthetic blend of host plant volatiles elicited equivalent levels of oriented upwind flight compared to the plants. Interestingly, wet cotton strips elicited low but consistent levels of upwind flight. In paired assays, only the synthetic blend paired with wet cotton strips elicited landing, although at significantly lower levels than that elicited by grape shoots. To achieve landing rates equivalent to live grape shoots, grape berry moth females required all three stimuli we tested: host odor cues, moisture, and visual cues simulating a grape shoot. These results suggest the cues have a synergistic effect, and that landing behavior requires complex sensory processing using multiple sensory inputs. Furthermore, these results suggest that moisture plays an important role in the host plant location process.     

The sensory basis of olfactory search behavior in banded kokopu (<Emphasis Type="Italic">Galaxias fasciatus</Emphasis>)

The sensory basis of olfactory search behavior was investigated in the banded kokopu, Galaxias fasciatus, using a flow tank. In the presence of a 2 cm s(-1) current flow, banded kokopu use both water current and chemical information to locate a food odor source. The superficial neuromasts of the lateral line system mediate the rheotactic component of the odor search. A physical block of one olfactory nostril did not affect the olfactory search strategy employed by banded kokopu in still water or in the presence of a current flow. Thus, there is no evidence that banded kokopu perform a bilateral comparison of the olfactory stimulus during their odor search. Previously, olfaction and gustation have been the only sensory systems shown to directly mediate orientation and movement towards odor sources in fish. The use of hydrodynamic cues by fish in location of an olfactory source has been previously proposed, but without direct experimental identification of the sensory systems employed. This study identifies the contributing roles of both olfactory and hydrodynamic sensory systems to the olfactory search repertoire of fish.     

The conflation of ignorance and knowledge in the inference of clade posteriors

The objective Bayesian approach relies on the construction of prior distributions that reflect ignorance. When topologies are considered equally probable a priori, clades cannot be. Shifting justifications have been offered for the use of uniform topological priors in Bayesian inference. These include: (i) topological priors do not inappropriately influence Bayesian inference when they are uniform; (ii) although clade priors are not uniform, their undesirable influence is negated by the likelihood function, even when data sets are small; and (iii) the influence of nonuniform clade priors is an appropriate reflection of knowledge. The first two justifications have been addressed previously: the first is false, and the second was found to be questionable. The third and most recent justification is inconsistent with the first two, and with the objective Bayesian philosophy itself. Thus, there has been no coherent justification for the use of nonflat clade priors in Bayesian phylogenetics. We discuss several solutions: (i) Bayesian inference can be abandoned in favour of other methods of phylogenetic inference; (ii) the objective Bayesian philosophy can be abandoned in favour of a subjective interpretation; (iii) the topology with the greatest posterior probability, which is also the tree of greatest marginal likelihood, can be accepted as optimal, with clade support estimated using other means; or (iv) a Bayes factor, which accounts for differences in priors among competing hypotheses, can be used to assess the weight of evidence in support of clades.
©The Willi Hennig Society 2009