Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence

We examine the representation of judgements of stochastic independence in probabilistic logics. We focus on a relational logic where (i) judgements of stochastic independence are encoded by directed acyclic graphs, and (ii) probabilistic assessments are flexible in the sense that they are not required to specify a single probability measure. We discuss issues of knowledge representation and inference that arise from our particular combination of graphs, stochastic independence, logical formulas and probabilistic assessments. (C) 2007 Elsevier B.V. All rights reserved.

Approximate algorithms for credal networks with binary variables

This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among experts; they can also encode models based on belief functions and possibilistic measures. All algorithms for approximate inference in this paper rely on exact inferences in credal networks based on polytrees with binary variables, as these inferences have polynomial complexity. We are inspired by approximate algorithms for Bayesian networks; thus the Loopy 2U algorithm resembles Loopy Belief Propagation, while the Iterated Partial Evaluation and Structured Variational 2U algorithms are, respectively, based on Localized Partial Evaluation and variational techniques. (C) 2007 Elsevier Inc. All rights reserved.

Estimating wave spectra from the motions of moored vessels: Experimental validation

The practicability of estimating directional wave spectra based on a vessel`s 1st order response has been recently addressed by several researchers. Different alternatives regarding statistical inference methods and possible drawbacks that could arise from their application have been extensively discussed, with an apparent preference for estimations based on Bayesian inference algorithms. Most of the results on this matter, however, rely exclusively on numerical simulations or at best on few and sparse full-scale measurements, comprising a questionable basis for validation purposes. This paper discusses several issues that have recently been debated regarding the advantages of Bayesian inference and different alternatives for its implementation. Among those are the definition of the best set of input motions, the number of parameters required for guaranteeing smoothness of the spectrum in frequency and direction and how to determine their optimum values. These subjects are addressed in the light of an extensive experimental campaign performed with a small-scale model of an FPSO platform (VLCC hull), which was conducted in an ocean basin in Brazil. Tests involved long and short crested seas with variable levels of directional spreading and also bimodal conditions. The calibration spectra measured in the tank by means of an array of wave probes configured the paradigm for estimations. Results showed that a wide range of sea conditions could be estimated with good precision, even those with somewhat low peak periods. Some possible drawbacks that have been pointed out in previous works concerning the viability of employing large vessels for such a task are then refuted. Also, it is shown that a second parameter for smoothing the spectrum in frequency may indeed increase the accuracy in some situations, although the criterion usually proposed for estimating the optimum values (ABIC) demands large computational effort and does not seem adequate for practical on-board systems, which require expeditious estimations. (C) 2009 Elsevier Ltd. All rights reserved.

Directional Wave Spectrum Estimation Based on Vessel`s 1st-order Motions: Field Results

In this paper, 2 different approaches for estimating the directional wave spectrum based on a vessel`s 1st-order motions are discussed, and their predictions are compared to those provided by a wave buoy. The real-scale data were obtained in an extensive monitoring campaign based on an FPSO unit operating at Campos Basin, Brazil. Data included vessel motions, heading and tank loadings. Wave field information was obtained by means of a heave-pitch-roll buoy installed in the vicinity of the unit. `two of the methods most widely used for this kind of analysis are considered, one based on Bayesian statistical inference, the other consisting of a parametrical representation of the wave spectrum. The performance of both methods is compared, and their sensitivity to input parameters is discussed. This analysis complements a set of previous validations based on numerical and towing-tank results and allows for a preliminary evaluation of reliability when applying the methodology at full scale.

Chautemsia calcicola: A new genus and species of Gloxinieae (Gesneriaceae) from Minas Gerais,.Brazil

A new species of Gesneriaceae discovered in remnants of deciduous forests on limestone outcrops in Minas Gerais, Brazil, is described and compared with morphologically related taxa. This plant presents the diagnostic features of the tribe Gloxinieae, but a unique combination of morphological traits distinguishes this taxon from previously described genera. Its phylogenetic position was inferred based on analyzing DNA sequences variation of five loci: the rpl1 intron, rps16 intron, trnL-F intron-spacer, a portion of the plastid-expressed glutamine synthetase gene (ncpGS) and the ribosomal DNA internal transcribed spacer (ITS). Molecular phylogenetic analyses confirm the position of this new species in the Gloxinieae, as a sister lineage of a clade including the Brazilian genera Mandirola and Goyazia. However, tests using topological constraints do not reject the alternative relationship that places this taxon with Gloxiniopsis in a monophyletic group. To accomodate this species in the current generic circumscription of gloxinieae, the new genus chautemsia A.O. Araujo V.C. Souza is created.

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model.

Phylogeography of Chelonus insularis (Hymenoptera: Braconidae) and Campoletis sonorensis (Hymenoptera: Ichneumonidae), Two Primary Neotropical Parasitoids of the Fall Armyworm (Lepidoptera: Noctuidae)

In a previous study, we observed no spatial genetic structure in Mexican populations of the parasitoids Chelonus insularis Cresson (Hymenoptera: Braconidae) and Campoletis sonorensis Cameron (Hymenoptera: Ichneumonidae) by using microsatellite markers In the current study, we Investigated whether for these important parasitoids of the fall armyworm (Lepidoptera: Noctuidae) there is any genetic structure at a larger scale Insects of both species were collected across the American continent and their phylogeography was Investigated using both nuclear and mitochondria] markers Our results suggest an ancient north-south migration of C insularis, whereas no clear pattern] could be determined for C sonorensis. Nonetheless, the resulting topology indicated the existence of a cryptic taxon within this later species. a few Canadian specimens determined as C. sonorensis branch outside a clack composed of the Argentinean Chelonus grioti Blanchard, the Brazilian Chelonus flavicincta Ashmead, and the rest of the C sonorensis individuals The individuals revealing the cryptic taxon were collected from Thichoplusia in (Hubner) (Lepidoptera. Noctuidae) on tomato (Lycopersicon spp) and may represent a biotype that has adapted to the early season phenology of its host. Overall, the loosely defined spatial genetic structure previously shown at a local fine scale also was found at the larger scale, for both species Dispersal of these insects may be partly driven by wind as suggested by genetic similarities between Individuals coming from very distant locations.

Genetic structure and biology of Xylella fastidiosa strains causing disease in citrus and coffee in Brazil

Xylella fastidiosa is a vector-borne, plant-pathogenic bacterium that causes disease in citrus (citrus variegated chlorosis [CVC]) and coffee (coffee leaf scorch [CLS]) plants in Brazil. CVC and CLS occur sympatrically and share leafhopper vectors; thus, determining whether X. fastidiosa isolates can be dispersed from one crop to another and cause disease is of epidemiological importance. We sought to clarify the genetic and biological relationships between CVC- and CLS-causing X. fastidiosa isolates. We used cross-inoculation bioassays and microsatellite and multilocus sequence typing (MLST) approaches to determine the host range and genetic structure of 26 CVC and 20 CLS isolates collected from different regions in Brazil. Our results show that citrus and coffee X. fastidiosa isolates are biologically distinct. Cross-inoculation tests showed that isolates causing CVC and CLS in the field were able to colonize citrus and coffee plants, respectively, but not the other host, indicating biological isolation between the strains. The microsatellite analysis separated most X. fastidiosa populations tested on the basis of the host plant from which they were isolated. However, recombination among isolates was detected and a lack of congruency among phylogenetic trees was observed for the loci used in the MLST scheme. Altogether, our study indicates that CVC and CLS are caused by two biologically distinct strains of X. fastidiosa that have diverged but are genetically homogenized by frequent recombination.

Does using stepwise variable selection to build sequential path analysis models make sense?

Causal inference methods - mainly path analysis and structural equation modeling - offer plant physiologists information about cause-and-effect relationships among plant traits. Recently, an unusual approach to causal inference through stepwise variable selection has been proposed and used in various works on plant physiology. The approach should not be considered correct from a biological point of view. Here, it is explained why stepwise variable selection should not be used for causal inference, and shown what strange conclusions can be drawn based upon the former analysis when one aims to interpret cause-and-effect relationships among plant traits.

Discourse priming of homophones in individuals with dominant subcortical lesions, cortical lesions, and Parkinson’s disease

An on-line priming experiment was used to investigate discourse-level processing in four matched groups of subjects: individuals with nonthalamic subcortical lesions (NSL) ( n =10), normal control subjects ( n =10), subjects with Parkinsons disease (PD) ( n =10), and subjects with cortical lesions ( n =10). Subjects listened to paragraphs that ended in lexical ambiguities, and then made speeded lexical decisions on visual letter strings that were: nonwords, matched control words, contextually appropriate associates of the lexical ambiguity, contextually inappropriate associates of the ambiguity, and inferences (representing information which could be drawn from the paragraphs but was not explicitly stated). Targets were presented at an interstimulus interval (ISI) of 0 or 1000ms. NSL and PD subjects demonstrated priming for appropriate and inappropriate associates at the short ISI, similar to control subjects and cortical lesion subjects, but were unable to demonstrate selective priming of the appropriate associate and inference words at the long ISI. These results imply intact automatic lexical processing and a breakdown in discourse-based meaning selection and inference development via attentional/strategic mechanisms.

A two-phase approach for detecting recombination in nucleotide sequences

Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. Delineating recombination events is important in the study of molecular evolution, as inference of such events provides a clearer picture of the phylogenetic relationships among different gene sequences or genomes. Nevertheless, detecting recombination events can be a daunting task, as the performance of different recombination-detecting approaches can vary, depending on evolutionary events that take place after recombination. We recently evaluated the effects of post-recombination events on the prediction accuracy of recombination-detecting approaches using simulated nucleotide sequence data. The main conclusion, supported by other studies, is that one should not depend on a single method when searching for recombination events. In this paper, we introduce a two-phase strategy, applying three statistical measures to detect the occurrence of recombination events, and a Bayesian phylogenetic approach in delineating breakpoints of such events in nucleotide sequences. We evaluate the performance of these approaches using simulated data, and demonstrate the applicability of this strategy to empirical data. The two-phase strategy proves to be time-efficient when applied to large datasets, and yields high-confidence results.

Sampling phylogenetic tree space with the generalized Gibbs sampler

The generalized Gibbs sampler (GGS) is a recently developed Markov chain Monte Carlo (MCMC) technique that enables Gibbs-like sampling of state spaces that lack a convenient representation in terms of a fixed coordinate system. This paper describes a new sampler, called the tree sampler, which uses the GGS to sample from a state space consisting of phylogenetic trees. The tree sampler is useful for a wide range of phylogenetic applications, including Bayesian, maximum likelihood, and maximum parsimony methods. A fast new algorithm to search for a maximum parsimony phylogeny is presented, using the tree sampler in the context of simulated annealing. The mathematics underlying the algorithm is explained and its time complexity is analyzed. The method is tested on two large data sets consisting of 123 sequences and 500 sequences, respectively. The new algorithm is shown to compare very favorably in terms of speed and accuracy to the program DNAPARS from the PHYLIP package.

A two-phase strategy for detecting recombination in nucleotide sequences

Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. Delineating recombination events is important in the study of molecular evolution, as inference of such events provides a clearer picture of the phylogenetic relationships among different gene sequences or genomes. Nevertheless, detecting recombination events can be a daunting task, as the performance of different recombination-detecting approaches can vary, depending on evolutionary events that take place after recombination. We previously evaluated the effects of post-recombination events on the prediction accuracy of recombination-detecting approaches using simulated nucleotide sequence data. The main conclusion, supported by other studies, is that one should not depend on a single method when searching for recombination events. In this paper, we introduce a two-phase strategy, applying three statistical measures to detect the occurrence of recombination events, and a Bayesian phylogenetic approach to delineate breakpoints of such events in nucleotide sequences. We evaluate the performance of these approaches using simulated data, and demonstrate the applicability of this strategy to empirical data. The two-phase strategy proves to be time-efficient when applied to large datasets, and yields high-confidence results.

Distinguishing species and populations of Rhipicephaline ticks with its 2 ribosomal RNA

Most populations and some species of ticks of the genera Boophilus (5 spp.) and Rhipicephalus (ca. 75 spp.) cannot be distinguished phenotypically. Moreover, there is doubt about the validity of species in these genera. I studied the entire second internal transcribed spacer (ITS 2) rRNA of 16 populations of rhipicephaline ticks to address these problems: Boophilus,microplus from Australia, Kenya, South Africa and Brazil (4 populations); Boophilus decoloratus from Kenya; Rhipicephalus appendiculatus from Kenya, Zimbabwe and Zambia (7 populations); Rhipicephalus zambesiensis from Zimbabwe (3 populations); and Rhipicephalus evertsi from Kenya. Each of the 16 populations had a unique ITS 2, but most of the nucleotide variation occurred among species and genera. ITS 2 rRNA can be used to distinguish the populations and species of Boophilus and Rhipicephalus studied here. Little support was found for the hypothesis that B. microplus from Australia and South Africa are different species. ITS 2 appears useful for phylogenetic inference in the Rhipicephalinae because in genetic distance, maximum likelihood, and maximum parsimony analyses, most branches leading to species had >95% bootstrap support. Rhipicephalus appendiculatus and R, zambeziensis are closely related, yet their ITS 2 sequences could be distinguished unambiguously. This lends weight to a previous proposal that Rhipicephalus sanguineus and Rhipicephalus turanicus, and Rhipicephalus pumlilio and Rhipicephalus camicasi, respectively, are conspecific, because each of these pairs of species had identical sequences for ca. 250 bp of ITS 2 rRNA.