Copper amendment of agricultural soil selects for bacterial antibiotic resistance in the field

AIMS: The objective of this study was to determine whether Cu-amendment of field plots affects the frequency of Cu resistance, and antibiotic resistance patterns in indigenous soil bacteria. METHODS AND RESULTS: Soil bacteria were isolated from untreated and Cu-amended field plots. Cu-amendment significantly increased the frequency of Cu-resistant isolates. A panel of isolates were characterized by Gram-reaction, amplified ribosomal DNA restriction analysis and resistance profiling against seven antibiotics. More than 95% of the Cu-resistant isolates were Gram-negative. Cu-resistant Gram-negative isolates had significantly higher incidence of resistance to ampicillin, sulphanilamide and multiple (> or =3) antibiotics than Cu-sensitive Gram-negative isolates. Furthermore, Cu-resistant Gram-negative isolates from Cu-contaminated plots had significantly higher incidence of resistance to chloramphenicol and multiple (> or =2) antibiotics than corresponding isolates from control plots. SIGNIFICANCE AND IMPACT OF THE STUDY: The results of this field experiment show that introduction of Cu to agricultural soil selects for Cu resistance, but also indirectly selects for antibiotic resistance in the Cu-resistant bacteria. Hence, the widespread accumulation of Cu in agricultural soils worldwide could have a significant effect on the environmental selection of antibiotic resistance.     

Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference

We propose an algorithm for selecting and clustering genes according to their time-course or dose-response profiles using gene expression data. The proposed algorithm is based on the order-restricted inference methodology developed in statistics. We describe the methodology for time-course experiments although it is applicable to any ordered set of treatments. Candidate temporal profiles are defined in terms of inequalities among mean expression levels at the time points. The proposed algorithm selects genes when they meet a bootstrap-based criterion for statistical significance and assigns each selected gene to the best fitting candidate profile. We illustrate the methodology using data from a cDNA microarray experiment in which a breast cancer cell line was stimulated with estrogen for different time intervals. In this example, our method was able to identify several biologically interesting genes that previous analyses failed to reveal.     

A method for reconstructing three-dimensional positions of swarming mosquitoes

We developed a new field method for reconstructing the three-dimensional positions of swarming mosquitoes. This method overcame certain inherent difficulties accompanied by conventional stereoscopic methods and is applicable to three-dimensional measurements of other insect species. Firstly, we constructed a probabilistic model for stereoscopy; if mosquitoes and six reference points with known coordinates were photographed simultaneously from two or more perspectives, then from the positions of images of mosquitoes and the reference points on the photographs, 1) the position of each camera with respect to the reference points is estimated; 2) stereo images which correspond to an identical real mosquito are matched; and 3) the spatial positions of the mosquitoes are determined. We automated the processes 1), 2) and 3), developing computer programs based on our model. We then constructed a portable system for three-dimensional measurements of swarming mosquitoes in the field. Initial data that illustrate the application of our method to studying mosquito swarming were presented.     

Recursive Residuals for Multivariate Field Processes

Vector recursive residuals are developed for multivariate regression models on a field. A vector response variable is observed at points on a rectangular grid, together with regression variables measured at the same points. Neighbouring values of the response vector may be correlated and simple models are considered using a direct product structure for the variance matrix. Subsequent to obtaining vector recursive residuals principal component analysis is applied to obtain an evaluation of any changes that may be occurring in the regression relationship over the field. The method is then applied to the problem of detecting zones of bush fire damage and recovery from LANDSAT data.     

Model selection for Cox models with time-varying coefficients

Summary Cox models with time-varying coefficients offer great flexibility in capturing the temporal dynamics of covariate effects on right-censored failure times. Because not all covariate coefficients are time varying, model selection for such models presents an additional challenge, which is to distinguish covariates with time-varying coefficient from those with time-independent coefficient. We propose an adaptive group lasso method that not only selects important variables but also selects between time-independent and time-varying specifications of their presence in the model. Each covariate effect is partitioned into a time-independent part and a time-varying part, the latter of which is characterized by a group of coefficients of basis splines without intercept. Model selection and estimation are carried out through a fast, iterative group shooting algorithm. Our approach is shown to have good properties in a simulation study that mimics realistic situations with up to 20 variables. A real example illustrates the utility of the method.     

A neural network model with feature selection for Korean speech act classification

A speech act is a linguistic action intended by a speaker. Speech act classification is an essential part of a dialogue understanding system because the speech act of an utterance is closely tied with the user's intention in the utterance. We propose a neural network model for Korean speech act classification. In addition, we propose a method that extracts morphological features from surface utterances and selects effective ones among the morphological features. Using the feature selection method, the proposed neural network can partially increase precision and decrease training time. In the experiment, the proposed neural network showed better results than other models using comparatively high-level linguistic features. Based on the experimental result, we believe that the proposed neural network model is suitable for real field applications because it is easy to expand the neural network model into other domains. Moreover, we found that neural networks can be useful in speech act classification if we can convert surface sentences into vectors with fixed dimensions by using an effective feature selection method.     

Retinal Vessel Width Measurement at Branchings Using an Improved Electric Field Theory-Based Graph Approach

The retinal vessel width relationship at vessel branch points in fundus images is an important biomarker of retinal and systemic disease. We propose a fully automatic method to measure the vessel widths at branch points in fundus images. The method is a graph-based method, in which a graph construction method based on electric field theory is applied which specifically deals with complex branching patterns. The vessel centerline image is used as the initial segmentation of the graph. Branching points are detected on the vessel centerline image using a set of detection kernels. Crossing points are distinguished from branch points and excluded. The electric field based graph method is applied to construct the graph. This method is inspired by the non-intersecting force lines in an electric field. At last, the method is further improved to give a consistent vessel width measurement for the whole vessel tree. The algorithm was validated on 100 artery branchings and 100 vein branchings selected from 50 fundus images by comparing with vessel width measurements from two human experts.     

A heuristic method for discovering biomarker candidates based on rough set theory

We apply a combined method of heuristic attribute reduction and evaluation of relative reducts in rough set theory to gene expression data analysis. Our method extracts as many relative reducts as possible from the gene-expression data and selects the best relative reduct from the viewpoint of constructing useful decision rules. Using a breast cancer dataset and a leukemia dataset, we evaluated the classification accuracy for the test samples and biological meanings of the rules. As a result, our method presented superior classification accuracy comparable to existing salient classifiers. Moreover, our method extracted interesting rules including a novel biomarker gene identified in recent studies. These results indicate the possibility that our method can serve as a useful tool for gene expression data analysis.     

Paradoxical hotspots for guanine oxidation by a chemical mediator of inflammation

Guanine in DNA is a major oxidation target owing to its low ionization potential (IP), and there is often an inverse correlation between damage frequency and sequence-dependent variation in guanine IP. We report that the biological oxidant nitrosoperoxycarbonate (ONOOCO2(-)) paradoxically selects guanines with the highest IP in GC-containing contexts. Along with sequence-dependent variation in damage chemistry, this behavior points to factors other than charge migration as determinants of genomic DNA oxidation.     

A New Method for the Analysis of Soft Tissues with Data Acquired under Field Conditions

Analyzing soft-tissue structures is particularly challenging due to the lack of homologous landmarks that can be reliably identified across time and specimens. This is particularly true when data are to be collected under field conditions. Here, we present a method that combines photogrammetric techniques and geometric morphometrics methods (GMM) to quantify soft tissues for their subsequent volumetric analysis. We combine previously developed methods for landmark data acquisition and processing with a custom program for volumetric computations. Photogrammetric methods are a particularly powerful tool for field studies as they allow for image acquisition with minimal equipment requirements and for the acquisition of the spatial coordinates of points (anatomical landmarks or others) from these images. For our method, a limited number of homologous landmarks, i.e., points that can be found on any specimen independent of space and time, and further distinctive points, which may vary over time, space and subject, are identified on two-dimensional photographs and their three-dimensional coordinates estimated using photogrammetric methods. The three-dimensional configurations are oriented by the spatial principal components (PCs) of the homologous points. Crucially, this last step orients the configuration such that x and y-information (PC1 and PC2 coordinates) constitute an anatomically-defined plane with the z-values (PC3 coordinate) in the direction of interest for volume computation. The z-coordinates are then used to estimate the volume of the tissue. We validate our method using a physical, geometric model of known dimensions and physical (wax) models designed to approximate perineal swellings in female macaques. To demonstrate the usefulness and potential of our method, we use it to estimate the volumes of Barbary macaque sexual swellings recorded in the field with video images. By analyzing both the artificial data and real monkey swellings, we validate our method''s accuracy and illustrate its potential for application in important areas of biological research.     

Modified histogram subtraction technique for analysis of flow cytometry data

Analysis of flow cytometry histogram data by the subjective selection of an integration window can be a tedious and time-consuming task and is often inaccurate. A new method for automated calculation of the percent positive from immunofluorescence histograms is presented. This new method is a modification of the currently used method of channel-by-channel histogram subtraction. Its accuracy is compared to that of the channel-by-channel histogram subtraction method and to another currently used automated method, which selects an integration window by finding the channels that contain the most fluorescent 2% of a control histogram. The new histogram subtraction method is objective, easy to use, and is more accurate than other currently used automated analysis methods. PASCAL source code is given for each method of analysis.     

Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology

Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this article was to identify an optimal predictive framework for restoration ecology using 11 modeling frameworks (including machine learning, inferential, and ensemble approaches) and three data groups (field data, geographic data [GIS], and a combination thereof). We test this approach with a dataset from a large postfire sagebrush reestablishment project in the Great Basin, U.S.A. Predictive power varied among models and data groups, ranging from 58% to 79% accuracy. Finer‐scale field data generally had the greatest predictive power, although GIS data were present in the best models overall. An ensemble prediction computed from the 10 models parameterized to field data was well above average for accuracy but was outperformed by others that prioritized model parsimony by selecting predictor variables based on rankings of their importance among all candidate models. The variation in predictive power among a suite of modeling frameworks underscores the importance of a model comparison and refinement approach that evaluates multiple models and data groups, and selects variables based on their contribution to predictive power. The enhanced understanding of factors influencing restoration outcomes accomplished by this framework has the potential to aid the adaptive management process for improving future restoration outcomes.     

Mechanisms of selection for drought stress tolerance and avoidance in Impatiens capensis (Balsaminaceae)

For longer lived annual plants, high water-use efficiency (WUE) and low stomatal conductance are hypothesized to confer a fitness advantage under drought stress. To directly test the adaptive significance of WUE and stomatal conductance under drought stress, inbred lines of Impatiens capensis were grown in two field environments (watered and not-watered), in a year of unusual early-season drought. In contrast to the results from a previous study of late-season drought in the same system, selection was detected for lower WUE, increased stomatal conductance, and early flowering time. These findings suggest that early-season drought conditions may select for drought avoidance traits such as low WUE and early reproduction, whereas later drought selects for tolerance traits such as high WUE.     

A practical approach for minimising inbreeding and maximising genetic gain in dairy cattle

A method that predicts the genetic composition and inbreeding (F) of the future dairy cow population using information on the current cow population, semen use and progeny test bulls is described. This is combined with information on genetic merit of bulls to compare bull selection methods that minimise F and maximise breeding value for profit (called APR in Australia). The genetic composition of the future cow population of Australian Holstein-Friesian (HF) and Jersey up to 6 years into the future was predicted. F in Australian HF and Jersey breeds is likely to increase by about 0.002 and 0.003 per year between 2002 and 2008, respectively. A comparison of bull selection methods showed that a method that selects the best bull from all available bulls for each current or future cow, based on its calf''s APR minus F depression, is better than bull selection methods based on APR alone, APR adjusted for mean F of prospective progeny after random mating and mean APR adjusted for the relationship between the selected bulls. This method reduced F of prospective progeny by about a third to a half compared to the other methods when bulls are mated to current and future cows that will be available 5 to 6 years from now. The method also reduced the relationship between the bulls selected to nearly the same extent as the method that is aimed at maximising genetic gain adjusted for the relationship between bulls. The method achieves this because cows with different pedigree exist in the population and the method selects relatively unrelated bulls to mate to these different cows. Selecting the best bull for each current or future cow so that the calf''s genetic merit minus F depression is maximised can slow the rate of increase in F in the population.     

SLEPR: a sample-level enrichment-based pathway ranking method -- seeking biological themes through pathway-level consistency

Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data.     

Investigating a simplified method for noninvasive genetic sampling in East African mammals using silica dried scat swabs

Swabbing scat has proved to be an effective noninvasive method to collect DNA from mammals in the field. Previously, this method has relied on preservative liquids or freezing to preserve the DNA collected on swabs. In this study, we determine the effectiveness of using silica to simply dry the swab in field as an alternative way to prevent DNA degredation. Four species were included in the study; reticulated giraffe, impala, fringe‐eared oryx, and lion. Swabs were taken at multiple time points for giraffe and impala scat samples, with the lion and oryx sampled opportunistically. Mitochondrial DNA was successfully amplified and sequenced from scat swabs from all species; however, effectiveness varied between species, with 81.8% amplification success rate from swabs taken from impala scat compared to 25% amplification success rate in giraffe. This variation in success rate was overcome by taking multiple swabs, thus increasing the probability of a successful amplification. The true merit of this method is in its simplicity and cheapness; no preservative liquids were required to be brought into the field, at no stage in the 2 weeks of field sampling were samples frozen, and no commercial kits were used for DNA extraction.     

Classification Performance-Based Feature Selection Algorithm for Machine Learning: P-Score

Feature selection algorithms are the cornerstone of machine learning. By increasing the properties of the samples and samples, the feature selection algorithm selects the significant features. The general name of the methods that perform this function is the feature selection algorithm. The general purpose of feature selection algorithms is to select the most relevant properties of data classes and to increase the classification performance. Thus, we can select features based on their classification performance. In this study, we have developed a feature selection algorithm based on decision support vectors classification performance. The method can work according to two different selection criteria. We tested the classification performances of the features selected with P-Score with three different classifiers. Besides, we assessed P-Score performance with 13 feature selection algorithms in the literature. According to the results of the study, the P-Score feature selection algorithm has been determined as a method which can be used in the field of machine learning.     

Research on the Top-Down Parsing Method for Context-Sensitive Graph Grammars

The parsing problem is one of the key problems of graph grammars. The typical parsing algorithm uses the bottom-up method. The time-complexity of this method is high, and it is difficult to apply. In order to reduce the time-complexity, this paper uses the top-down method for parsing. This method avoids the subgraph isomorphism judgment and selects the productions specifically, so that the time-complexity is greatly reduced.     

Spatial frequency tuned channels: implications for structure and function from psychophysical and computational studies of stereopsis

Various psychophysical experiments investigating the role of spatial frequency tuned channels in stereopsis are reviewed and a computational model of stereopsis deriving from these studies is described. The distinctive features of the model are: (1) it identifies edge locations in each monocular field by searching for zero crossings in non-orientated centre-surround convolution profiles; (2) it selects among all possible binocular point-for-point combinations of edge locations only those which satisfy a (quasi-) collinear figural grouping rule; (3) it presents a concept of the orientated and spatial frequency tuned channel as a nonlinear grouping operator. The success of the model is demonstrated both on a stereo pair of a natural scene and on a random-dot stereogram.     

The evolution of intermediate castration virulence and ant coexistence in a spatially structured environment

Theory suggests that spatial structuring should select for intermediate levels of virulence in parasites, but empirical tests are rare and have never been conducted with castration (sterilizing) parasites. To test this theory in a natural landscape, we construct a spatially explicit model of the symbiosis between the ant-plant Cordia nodosa and its two, protecting ant symbionts, Allomerus and Azteca . Allomerus is also a castration parasite, preventing fruiting to increase colony fecundity. Limiting the dispersal of Allomerus and host plant selects for intermediate castration virulence. Increasing the frequency of the mutualist, Azteca , selects for higher castration virulence in Allomerus , because seeds from Azteca -inhabited plants are a public good that Allomerus exploits. These results are consistent with field observations and, to our knowledge, provide the first empirical evidence supporting the hypothesis that spatial structure can reduce castration virulence and the first such evidence in a natural landscape for either mortality or castration virulence.